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首次观测 · 记录 #1CVF · CVPR 2026:cvf:14166061896bed8b0704ebb86cb0907aCVF · CVPR 2026

BiPA: Bilevel Prompt Adaptation for Underwater Instance Segmentation

Ma, Long, Zheng, Haoze, Mao, Yuhang, Liu, Jinyuan, Xu, Chengpei, Xue, Xinwei, Wang, Yi, He, Xiangjian 等 9 位作者

原文摘要

Underwater instance segmentation is essential for fine-grained scene understanding. However, underwater imagery exhibits a strong domain gap from in-air vision due to severe degradation (e.g., turbidity). Consequently, despite its general segmentation ability, SAM degrades sharply underwater. In this work, we propose BiPA, which effectively adapts SAM to the underwater domain. To be concrete, we construct an underwater SAM with dual prompts and introduce a foreground-attentive injection block to enhance local foreground representation. We formulate dense prompt learning as a bilevel optimization, explicitly capturing the mutual dependency between prompt and model. To make this tractable, we design a two-stage learning strategy. The first stage adapts the dense prompt itself, updating it with Bayesian optimization to learn efficiently. The second stage fine-tunes the model parameters under the frozen optimized prompt, which finally enables effective cross-domain adaptation. Extensive experiments and analyses verify the superiority and efficiency of BiPA. The code is publicly available at https://github.com/ZeAstra/BiPA.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1421783c9a499f056fd23a85b01fa2e4CVF · CVPR 2026

TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Zhu, Haowei, Huang, Tingxuan, Wang, Xing, Zhao, Tianyu, Wang, Jiexi, Chen, Weifeng, Peng, Xurui, Chen, Fangmin 等 10 位作者

原文摘要

Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (TAP), a training-free, probe-driven framework that adaptively selects a predictor for each token at every sampling step. TAP uses a single full evaluation of the model's first layer as a low-cost probe to compute proxy losses for a compact family of candidate predictors (instantiated primarily with Taylor expansions of varying order and horizon), then assigns each token the predictor with the smallest proxy error. This per-token "probe-then-select" strategy exploits heterogeneous temporal dynamics, requires no additional training, and is compatible with various predictor designs. TAP incurs negligible overhead while enabling large speedups with little or no perceptual quality loss. Extensive experiments across multiple diffusion architectures and generation tasks show that TAP substantially improves the accuracy-efficiency frontier compared to fixed global predictors and caching-only baselines.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14389fcb3578c0bddbfc1942d161035fCVF · CVPR 2026

Rethinking Glyph Spatial Information in Font Generation

Su, Peng, Yang, Xi

原文摘要

Few-shot Font Generation (FFG) aims to create a complete font from a limited number of references, offering significant practical value. However, existing methods neglect glyph spatial information, which leads to two critical limitations. At the pipeline level, distorted rendering introduces spatial bias, impairing vectorization and dataset quality, and this problem is compounded by the lack of unified standards, which undermines a unified benchmark. At the model level, the implicit coupling of shape and position hinders fine-grained optimization and generalization. We address these challenges in the context of Chinese font generation, where glyph complexity demands superior model capability. Consequently, we first propose a Spatial-Preserving Rendering (SPR) scheme, which eliminates spatial bias and enables accurate vectorization. Alongside, we release an OFL-licensed Chinese font dataset to establish a unified benchmark. Then, technically, we propose GlyphSpatialNet, a two-stage framework to explicitly model glyph spatial information in pixel space. In first stage, we design a Shape-Position Decoupling (SPD) architecture and a Gradient Broadcasting Module (GBM) to achieve font style transfer in low resolution. In second stage, we design Style Detail Enhancement (SDE), which refines the style details for high resolution outputs. Extensive experiments demonstrate the effectiveness of our approach. Code and dataset are available at https://github.com/sp777g/GlyphSpatialNet.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1457a0272552a3d0c0c7578b0f73ea6dCVF · CVPR 2026

FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts

Li, You, Zhou, Dewei, Ma, Fan, Li, Fu, He, Dongliang, Yang, Yi

原文摘要

Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they struggle with fine-grained temporal control in multi-event scenarios or when visual cues are insufficient, such as small regions, off-screen sounds, or occluded/partially visible objects.In this paper, we propose FoleyDirector, a framework that, for the first time, enables precise temporal guidance in DiT-based V2A generation while preserving the base model's audio quality and allowing seamless switching between V2A generation and temporally controlled synthesis. FoleyDirector introduces Structured Temporal Scripts(STS), a set of captions corresponding to short temporal segments, to provide richer temporal information. These features are integrated via the Script-Guided Temporal Fusion Module, which employs Temporal Script Attention to fuse STS features coherently. To handle complex multi-event scenarios, we further propose Bi-Frame Sound Synthesis, enabling parallel in-frame and out-of-frame audio generation and improving controllability.To support training and evaluation, we construct the DirectorSound dataset and introduce VGGSound-Director and DirectorBench. Experiments demonstrate that FoleyDirector substantially enhances temporal controllability while maintaining high audio fidelity, empowering users to act as Foley directors and advancing V2A toward more expressive and controllable.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1457bddde36b03a31b5769fad65a6cd3CVF · CVPR 2026

FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction

Liu, Yuqiu, Song, Jialin, de Chanlatte, Marissa Ramirez, Chowdhury, Rochishnu, Desai, Rushil Paresh, Chen, Wuyang, Martin, Daniel, Mahoney, Michael W.

原文摘要

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the image or representation space. This appearance-centric view overlooks body contacts and couplings, conflates function-critical regions (e.g., aerodynamic or hydrodynamic surfaces) with ornamentation, and reconstructs structures suboptimally, even when physical regularizers are added. All these can lead to unphysical and implausible interactions. To address this, we consider the question: How can 3D reconstruction become aware of real-world interactions and underlying object functionality, beyond visual cues? To answer this question, we propose FluidGaussian, a plug-and-play method that tightly couples geometry reconstruction with ubiquitous fluid-structure interactions to assess surface quality at high granularity. We define a simulation-based uncertainty metric induced by fluid simulations and integrate it with active learning to prioritize views that improve both visual and physical fidelity. In an empirical evaluation on NeRF Synthetic (Blender), Mip-NeRF 360, and DrivAerNet++, our FluidGaussian method yields up to +8.6% visual PSNR (Peak Signal-to-Noise Ratio) and -62.3% velocity divergence during fluid simulations. Our code is available in https://github.com/delta-lab-ai/FluidGaussian.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14593b1537c1a697d8d358adbb73eaaeCVF · CVPR 2026

DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities

Lu, Jueqing, Qi, Yuanyuan, Yang, Xiaohao, Niu, Shuaicheng, Ke, Fucai, Zhou, Shujie, Tan, Wei, Lin, Jionghao 等 11 位作者

原文摘要

The performance of Vision-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete information. Existing missing-aware prompt methods help reduce this degradation, but they still rely on conventional prediction heads (e.g., a Fully-Connected layer) that compute class scores in the same way regardless of which modality is present or absent. We introduce Decoupled Prototype Learning (DPL), a new prediction head architecture that explicitly adjusts its decision process to the observed input modalities. For each class, DPL selects a set of prototypes specific to the current missing-modality cases (image-missing, text-missing, or mixed-missing). Each prototype is then decomposed into image-specific and text-specific components, enabling the head to make decisions that depend on the information actually present. This adaptive design allows DPL to handle inputs with missing modalities more effectively while remaining fully compatible with existing prompt-based frameworks. Extensive experiments on MM-IMDb, UPMC Food-101, and Hateful Memes demonstrate that DPL outperforms state-of-the-art approaches across all widely used multimodal image-text datasets and various missing cases.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:146aabffbbeb81f22d1b5ab02f4fba6cCVF · CVPR 2026

Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis

Jiang, Yinuo, Cheng, Jun, Wang, Yiran, Cheng, Cheng

原文摘要

Neural Radiance Fields (NeRF) have shown remarkable success in image novel view synthesis (NVS), inspiring extensions to LiDAR NVS. However, most methods heavily rely on accurate camera poses for scene reconstruction. The sparsity and textureless nature of LiDAR data also present distinct challenges, leading to geometric holes and discontinuous surfaces. To address these issues, we propose SG-NLF, a pose-free LiDAR NeRF framework that integrates spectral information with geometric consistency. Specifically, we design a hybrid representation based on spectral priors to reconstruct smooth geometry. For pose optimization, we construct a confidence-aware graph based on feature compatibility to achieve global alignment. In addition, an adversarial learning strategy is introduced to enforce cross-frame consistency, thereby enhancing reconstruction quality. Comprehensive experiments demonstrate the effectiveness of our framework, especially in challenging low-frequency scenarios. Compared to previous state-of-the-art methods, SG-NLF improves reconstruction quality and pose accuracy by over 35.8% and 68.8%. Our work can provide a novel perspective for LiDAR view synthesis.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:14738b1c9f8e32b6413ba684dc079542CVF · CVPR 2026

VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale

Kulkarni, Parth Parag, Gupta, Rohit, Chhipa, Prakash Chandra, Shah, Mubarak

原文摘要

The task of video geolocalization aims to determine the precise GPS coordinates of a video's origin and map its trajectory; with applications in forensics, social media, and exploration. Existing classification-based approaches operate at a coarse city-level granularity and fail to capture fine-grained details, while image retrieval methods are impractical on a global scale due to the need for extensive image galleries which are infeasible to compile. Comparatively, constructing a gallery of GPS coordinates is straightforward and inexpensive. We propose VidTAG, a dual-encoder framework that performs frame-to-GPS retrieval using both self-supervised and language-aligned features. To address temporal inconsistencies in video predictions, we introduce the TempGeo module, which aligns frame embeddings, and the GeoRefiner module, an encoder-decoder architecture that refines GPS features using said aligned frame embeddings. Evaluations on Mapillary (MSLS) and GAMa datasets demonstrate our model's ability to generate temporally consistent trajectories and outperform baselines, achieving a 20% improvement at the 1 km threshold over GeoCLIP. We also beat current State-of-the-Art by 25% on global coarse grained video geolocalization (CityGuessr68k). Our approach enables fine-grained video geolocalization and lays a strong foundation for future research. More details on the project webpage: parthpk.github.io/vidtag webpage.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14868e69f6facf767dae0e3f639e8780CVF · CVPR 2026

A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World

Cheng, Jikang, Yan, Renye, Yan, Zhiyuan, Gan, Yaozhong, Zhang, Xueyi, Wang, Zhongyuan, Peng, Wei, Liang, Ling

原文摘要

Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" could be the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity, advancement, and vast volume of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications.However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC).In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a two-stage, model-agnostic framework termed DevDet (\underline Dev eloper for \underline Det ector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in effectively predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:149c8d254f0543ba60595befe6c1e2f7CVF · CVPR 2026

MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA

Gu, Haowen, Pei, Gensheng, Sun, Zeren, Ren, Mingwu, Shu, Xiangbo, Yao, Yazhou, Shen, Fumin

原文摘要

Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational demands of existing large vision-language models. We propose MedFG-VQA, a lightweight framework that leverages a memory bank to augment DCT-based low-frequency features and employs graph-enhanced cross-attention for effective visual-textual alignment. Specifically, our approach features two key components: Frequency-Memory Fusion (FMF), which enhances low-frequency features by retrieving from a learnable memory bank built on DCT decomposition, and Graph-Aware Cross-Attention (GACA), which aligns visual-textual features via cross-attention and refines them through graph-convolutional aggregation. To address data scarcity, we construct SynMed-VQA, a large-scale synthetic dataset comprising over 2 million question-answer pairs across 9 imaging modalities and 10 major organs, generated with GPT-4o. Extensive experiments on SynMed-VQA and three other standard biomedical VQA benchmarks demonstrate that MedFG-VQA achieves competitive or superior performance compared to much larger models while maintaining significantly lower computational costs, highlighting its efficiency and potential for clinical deployment.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:149cdc6cefa5d3669d8965cae1776750CVF · CVPR 2026

FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants

Bhosale, Mahesh, Wasi, Abdul, Srivastava, Shantam, Latif, Shifa, Luan, Tianyu, Gao, Mingchen, Doermann, David, Gong, Xuan

原文摘要

While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In safety-critical clinical settings, such disparities risk producing unequal diagnostic narratives and eroding trust in AI-assisted decision-making. While fairness has been studied extensively in vision-only and language-only models, its impact on MLLMs remains largely underexplored. To address these biases, we introduce FairLLaVA, a parameter-efficient fine-tuning method that mitigates group disparities in visual instruction tuning without compromising overall performance. By minimizing the mutual information between target attributes, FairLLaVA regularizes the model's representations to be demographic-invariant. The method can be incorporated as a lightweight plug-in, maintaining efficiency with low-rank adapter fine-tuning, and provides an architecture-agnostic approach to fair visual instruction following. Extensive experiments on large-scale chest radiology report generation and dermoscopy visual question answering benchmarks show that FairLLaVA consistently reduces inter-group disparities while improving both equity-scaled clinical performance and natural language generation quality across diverse medical imaging modalities. Code can be accessed at https://github.com/bhosalems/FairLLaVA.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14be1fb2a0e785a9c988d3658a7f7d70CVF · CVPR 2026

Curriculum Group Policy Optimization: Adaptive Sampling for Unleashing the Potential of Text-to-Image Generation

Li, Baoteng, Zang, Xianghao, Wang, Xinran, Na, Xiangyu, He, Zhixiang, Sun, Hao, Zhang, Chi, He, Zhongjiang 等 11 位作者

原文摘要

Text-to-Image (T2I) generation has achieved remarkable progress in recent years. Meanwhile, reinforcement learning methods, particularly those based on Group Relative Policy Optimization (GRPO), have attracted widespread attention and been successfully applied to T2I tasks. However, the uniform sampling strategy commonly used during training often ignores the match between sample difficulty and the model's current learning capability, leading to low training efficiency. We argue that improving training efficiency requires continuously prioritizing prompts that match the model's evolving capability and remain actively learnable. To this end, we propose Curriculum Group Policy Optimization (CGPO), an adaptive curriculum training framework. During training, each prompt produces a group of images scored by a reward model. We use the variance of group rewards as an online proxy for prompt inconsistency. A higher variance suggests that the model has partially captured the prompt requirements but has not yet achieved stable mastery. Such prompts are more likely to provide useful learning signals, so we increase their sampling probabilities accordingly. Additionally, to address data imbalance in multi-category datasets, we design a category calibration method based on proportional fairness optimization, which balances training difficulty across categories. Experiments on GenEval, T2I-CompBench++, and DPG Bench demonstrate that our framework effectively improves generation performance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14d252f7c622eb85f282ab21c8845804CVF · CVPR 2026

Training-free Motion Factorization for Compositional Video Generation

Wang, Zixuan, Zhou, Ziqin, Chen, Feng, Peng, Duo, Hu, Yixin, Li, Changsheng, Lei, Yinjie

原文摘要

Compositional video generation aims to synthesize multiple instances with diverse appearance and motion. However, current approaches mainly focus on binding semantics, neglecting to understand diverse motion categories specified in prompts. In this paper, we propose a motion factorization framework that decomposes complex motion into three primary categories: motionlessness, rigid motion, and non-rigid motion. Specifically, our framework follows a planning before generation paradigm. (1) During planning, we reason about motion laws on the motion graph to obtain frame-wise changes in the shape and position of each instance. This alleviates semantic ambiguities in the user prompt by organizing it into a structured representation of instances and their interactions. (2) During generation, we modulate the synthesis of distinct motion categories in a disentangled manner. Conditioned on the motion cues, guidance branches stabilize appearance in motionless regions, preserve rigid-body geometry, and regularize local non-rigid deformations. Crucially, our two modules are model-agnostic, which can be seamlessly incorporated into various diffusion model architectures. Extensive experiments demonstrate that our framework achieves impressive performance in motion synthesis on real-world benchmarks. Code is available at https://github.com/ZixuanWang0525/MF-CVG.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14da77d201599dfd578f8fd1e3a19205CVF · CVPR 2026

MaxMark: High-Capacity Diffusion-Native Watermarking via Robust and Invertible Latent Embedding

Chang, Xuanhang, Yang, Zhonghao, Zhuo, Cheng, Li, Yu

原文摘要

Diffusion-native watermarking provides a more secure and reliable way to trace images from latent diffusion models (LDMs) by embedding information directly into the generative process. However, existing methods suffer from a fundamental limitation: their embedding capacity is extremely small. We introduce MaxMark, a high-capacity watermarking framework that supports embed rich watermark messages into generated images. MaxMark uses two components: a robust watermark embedding module that enhance the secret message and places them into reliable regions of the latent noise, and a distribution transformation module that maps the watermarked latent back to an approximate Gaussian, ensuring compatibility with the diffusion process and preserving image fidelity. The distribution transformation is implemented with an invertible neural network (INN), whose exactly reversible structure enables precise recovery and efficient training. Experiments show that MaxMark surpasses prior methods in capacity, robustness, and imperceptibility, achieving up to a 46% improvement in bit accuracy for large watermark payloads.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14fc697ca40073dbf7ca2ee57cc81f18CVF · CVPR 2026

Scenes as Tokens: Multi-Scale Normal Distributions Transform Tokenizer for General 3D Vision-Language Understanding

Tang, Yutao, Zhao, Cheng, Mittal, Gaurav, Kukkala, Rohith, Chellappa, Rama, Peng, Cheng, Chen, Mei

原文摘要

Recent advances in 3D vision-language models (VLMs) highlight a strong potential for 3D scene understanding and reasoning.However, effectively tokenizing 3D scenes into holistic scene tokens, and leveraging these tokens across diverse 3D understanding tasks, remain highly challenging. We present NDTokenizer3D, a generalist 3D VLM that performs a wide range of 3D scene understanding tasks while naturally supporting human interactions, thereby bridging language-level reasoning with 3D spatial understanding. The core of our approach is a novel three-stage scene tokenization pipeline built upon a Multi-Scale Normal Distributions Transform (NDT) representation, paired with a Multi-Scale NDT Decoder (MSDec).Specifically, NDTokenizer3D first constructs a multi-scale NDT representation from raw high-resolution point clouds, preserving both global context and fine-grained geometric details. Next, the MSDec progressively fuses cross-scale NDT features, producing holistic scene tokens consumable by LLM endpoints. Beyond tokenization, MSDec is repurposed as a general interface for human-interactive prompting (points, boxes, masks) and segmentation-mask decoding, unifying diverse 3D scene understanding tasks within a single architecture. With this compact and unified design, NDTokenizer3D offers a fine-grained, general-purpose 3D VLM, achieving remarkable improvements in 3D Referring Segmentation, 3D Visual Question Answering, and 3D Dense Captioning.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:14fe67ddf1bd05b71861b61ca376cf8dCVF · CVPR 2026

Localizing, Structuring, and Rendering: Bridging 3D and 2D Vision-Language-Action Models for Robotic Manipulation

Zhao, Yunlong, Deng, Xiaoheng, Cao, Yichao, Chen, Yi, He, Xiangjian, You, Shan, Yang, Shuo, Fan, Lei 等 10 位作者

原文摘要

Robotic manipulation in complex 3D environments requires unifying spatial reasoning with intuitive visual perception, which is a capability that current Vision-Language-Action paradigms address separately. While 3D VLAs excel in geometric and physical reasoning, they lack intuitive, image-level understanding and dense visual semantics; conversely, 2D VLAs (even with depth image) provide rich visual intuition and semantic continuity but miss explicit spatial global grounding. We introduce DiffRender-VLA, a differentiable rendering-based framework that bridges 3D and 2D Vision-Language-Action models through gradient-consistent visual mediation. It generates differentiable images by localizing the next end-effector target with a world-aligned cube marker, differentiably structuring surrounding geometry whose color encodes spatial relations to the marker, and rendering adaptive viewpoints optimized to reveal the target-environment spatial relationships. These differentiable images serve as visual bridges, embedding spatial semantics while allowing gradients from 2D VLAs to backpropagate into 3D representations, thereby coupling geometric reasoning with visual perception. This closed differentiable loop unifies reasoning and perception, substantially improving performance under occlusion, clutter, and complex spatial manipulation tasks, achieving average improvements of +12.1% over state-of-the-art methods. Codes are available at https://github.com/zyl123456aB/DIFFVLA.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1512727c51cd0892e2ad39c329ddcff4CVF · CVPR 2026

PrivateEyes: Gaze-Preserving Anonymization for Data Sharing

Gupta, Surabhi, Muthumariappan, Dinesh Prabhu, Das, Biplab, Rajagopal, Anoop Kolar, Iyer, Kiran Nanjunda, Seo, Donghwan

原文摘要

Eye images captured from wearable devices such as head-mounted displays (HMDs) contain identifiable biometric cues, posing significant challenges for safe data sharing. Existing eye anonymization techniques often degrade downstream performance, particularly gaze estimation while still retaining iris-recognizable features. Although these methods aim to anonymize the iris, they introduce noticeable visual artifacts that reduce image fidelity. To address these limitations, we propose PrivateEyes, a privacy-preserving framework that synthesizes anonymized yet gaze-consistent eye images. Our approach employs a three-stage pipeline: (1) a deep segmentation network that isolates semantic eye regions and provides structural control signals for synthesis, (2) a pose estimation network (PEN) trained on anatomically accurate synthetic eye renders to infer precise eye pose, and (3) a conditional diffusion model that reconstructs realistic, anonymized eye images conditioned on segmentation and pose. Extensive experiments across multiple benchmark datasets show that PrivateEyes achieves superior gaze-estimation accuracy compared to state-of-the-art anonymization baselines, improving performance by over 10% while reducing iris-recognition accuracy by ~50%. Our method also produces higher-fidelity images compared to other existing approaches. By enabling task-preserving and privacy-secure sharing of eye images, PrivateEyes supports responsible research and development in AR/VR and other gaze-driven applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MU-GeNeRF: Multi-view Uncertainty-guided Generalizable Neural Radiance Fields for Distractor-aware Scene

Mu, Wenjie, Li, Zhan, Su, Chuanzhou, Shen, Xuanyi, Liu, Ziniu, Lu, Fan, Mo, Yujian, Zhao, Junqiao 等 11 位作者

原文摘要

Generalizable Neural Radiance Fields (GeNeRF) enable high-quality scene reconstruction from a limited number of views and can generalize to unseen scenes. However, in real-world environments, transient distractors disrupt structural consistency across views, leading to deviated supervision signals and degraded reconstruction quality. Existing distractor-free NeRF methods rely on per-scene optimization and they estimate uncertainty from per-view reconstruction errors to remove distractors, but this is unreliable to GeNeRF, because it may misjudge inconsistent static structures from source views as distractors. To address this issue, we propose MUGeNeRF: a multi-view uncertainty-guided distractor-aware GeNeRF method, aim to effectively alleviate GeNeRF's robust modeling challenges in dynamic scenes with transient distractions. We explicitly decompose distractor awareness into two complementary uncertainty modeling tasks: Source-view uncertainty, serving as a transferable prior during the feed-forward process, captures structural inconsistencies across source views caused by viewpoint changes or dynamic factors; Target-view uncertainty focuses on observation anomalies caused by transient changes to infer distractor spatial distribution. These two uncertainties are integrated into a heteroscedastic reconstruction loss that guides adaptive supervision weighting, boosting the model's capability to detect and suppress distractors, and enabling more robust geometric modeling. Extensive experiments demonstrate that our method not only outperforms existing GeNeRF approaches but also rivals the performance of scene-specific distractor-free NeRFs.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models

Wang, Shengao, Wang, Wenqi, Wang, Zecheng, Whitton, Max, Wakeham, Michael, Chandra, Arjun, Huang, Joey, Zhu, Pengyue 等 23 位作者

原文摘要

Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded framework for infant-inspired vision-language modeling that extensively improves upon BabyVLM-V1 through a longitudinal, multifaceted pretraining set, a versatile model, and, most importantly, DevCV Toolbox for cognitive evaluation. The pretraining set maximizes coverage while minimizing curation of a longitudinal, infant-centric audiovisual corpus, yielding video-utterance, image-utterance, and multi-turn conversational data that mirror infant experiences. DevCV Toolbox adapts all vision-related measures of the recently released NIH Baby Toolbox into a benchmark suite of ten multimodal tasks, covering spatial reasoning, memory, and vocabulary understanding aligned with early children's capabilities. Experimental results show that a compact model pretrained from scratch can achieve competitive performance on DevCV Toolbox, outperforming GPT-4o on some tasks. We hope the principled, unified BabyVLM-V2 framework will accelerate research in developmentally plausible pretraining of vision foundation models.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

He, Boyu, Ye, Yunfan, Liu, Chang, Wu, Weishang, Liu, Fang, Cai, Zhiping

原文摘要

Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints (e.g., semantic masks) restricting applicability; 3) rigid feature associations lacking adaptive global-local alignment, failing to balance fine-grained stylization and global content preservation. These limitations, particularly the inability to flexibly leverage style inputs, fundamentally restrict style transfer in terms of personalization, accuracy, and adaptability.To address these, we propose StyleGallery, a training-free and semantic-aware framework that supports arbitrary reference images as input and enables effective personalized customization.It comprises three core stages: semantic region segmentation (adaptive clustering on latent diffusion features to divide regions without extra inputs); clustered region matching (block filtering on extracted features for precise alignment); and style transfer optimization (energy function-guided diffusion sampling with regional style loss to optimize stylization).Experiments on our introduced benchmark demonstrate that StyleGallery outperforms state-of-the-art methods in content structure preservation, regional stylization, interpretability, and personalized customization, particularly when leveraging multiple style references. The source code and dataset are available at https://github.com/iiiiiiiword/StyleGallery.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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X-PCR: A Benchmark for Cross-modality Progressive Clinical Reasoning in Ophthalmic Diagnosis

Wang, Gui, Zhong, Zehao, Zhou, YongSong, Li, Yudong, Wu, Ende, Cheah, Wooi Ping, Qu, Rong, Ren, Jianfeng 等 9 位作者

原文摘要

Despite significant progress in Multi-modal Large Language Models (MLLMs), their clinical reasoning capacity for multi-modal diagnosis remains largely unexamined. Current benchmarks, mostly single-modality data, can't evaluate progressive reasoning and cross-modal integration essential for clinical practice. We introduce the Cross-Modality Progressive Clinical Reasoning (X-PCR) benchmark, the first comprehensive evaluation of MLLMs through a complete ophthalmology diagnostic workflow, with two reasoning tasks: 1) a six-stage progressive reasoning chain spanning image quality assessment to clinical decision-making, and 2) a cross-modality reasoning task integrating six imaging modalities. The benchmark comprises 26,415 images and 177,868 expert-verified VQA pairs curated from 51 public datasets, covering 52 ophthalmic diseases. Evaluation of 21 MLLMs reveals critical gaps in progressive reasoning and cross-modal integration. Dataset and code: https://github.com/CVI-SZU/X-PCR.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MeteorPred: A Meteorological Multimodal Large Model and Dataset for Severe Weather Event Prediction

Tang, Shuo, Xu, Jian, Zhang, Jiadong, Chen, Yi, Jin, Qizhao, Shen, Lingdong, Liu, Chenglin, Xiang, Shiming

原文摘要

Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events prediction still depends on subjective, time-consuming expert interpretation, end-to-end "AI weather station" systems are emerging but face three major challenges: (1) scarcity of severe weather event samples; (2) imperfect alignment between high-dimensional meteorological data and textual warnings; (3) current multimodal language models cannot effectively process high-dimensional meteorological inputs or capture their complex spatiotemporal dependencies. To address these challenges, we introduce MP-Bench, the first large-scale multimodal dataset for severe weather events prediction, comprising 421,363 pairs of raw multi-year meteorological data and corresponding text caption, covering a wide range of severe weather scenarios. On top of this dataset, we develop a Meteorology Multimodal Large Model (MMLM) that directly ingests 4D meteorological inputs. In addition, it is designed to accommodate the unique characteristics of 4D meteorological data flow, incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions. Extensive experiments on MP-Bench show that MMLM achieves strong performance across multiple tasks, demonstrating effective severe weather understanding and representing a key step toward automated, AI-driven severe weather events forecasting systems.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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FeatureFool: Zero-Query Fooling of Video Models via Feature Map

Tang, Duoxun, Xiao, Xi, Hu, Guangwu, Sun, Kangkang, Yang, Xiao, Chen, Dongyang, Li, Qing, Yin, Yong-jie 等 9 位作者

原文摘要

The vulnerability of deep neural networks (DNNs) has been preliminarily verified. Existing black-box adversarial attacks usually require multi-round interaction with the model and consume numerous queries, which is impractical in the real-world and hard to scale to recently emerged Video-LLMs. Moreover, no attack in the video domain directly leverages feature maps to shift the clean-video feature space. We therefore propose FeatureFool, a stealthy, video-domain, zero-query black-box attack that utilizes information extracted from a DNN to alter the feature space of clean videos. Unlike query-based methods that rely on iterative interaction, FeatureFool performs a zero-query attack by directly exploiting DNN-extracted information. This efficient approach is unprecedented in the video domain. Experiments show that FeatureFool achieves an attack success rate above 70% against traditional video classifiers without any queries. Benefiting from the transferability of the feature map, it can also craft harmful content and bypass Video-LLM recognition. Additionally, adversarial videos generated by FeatureFool exhibit high quality in terms of SSIM, PSNR, and Temporal-Inconsistency, making the attack barely perceptible. This paper may contain violent or explicit content.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Adaptive 3D Perception for Small Aerial Targets Under Sparse Sampling via Reinforcement Learning

Yuan, Shenghai, Yihan, Wei, Yee, Jason, Qiao, Zhuoran, lou, boyang, Hu, Enwen

原文摘要

Detecting small aerial targets (SATs) in long-range LiDAR is challenging because motion causes extreme variations in point density, breaking fixed-voxel and static-threshold assumptions in standard 3D detection and tracking. To address the challenges, we introduce A3PRL, an RL-driven adaptive perception framework that closes the loop between LiDAR sensing and tracking. A3PRL uses a sparsity-aware proposal stage with Temporal Dispersion Signatures and velocity-change cues, and a lightweight 5D policy that jointly adjusts voxel resolution, detection sensitivity, and association gating from label-free statistics of sparsity, foreground acceptance, and track stability. The policy is trained with privileged ground-truth trajectories to optimize a reward balancing geometric accuracy, temporal stability, and regularized acceptance, but runs fully label-free at test time. On the public MMAUD benchmark, training on V1 and evaluating on unseen V2/V3, A3PRL reduces 3D localization error by about 19% over its non-RL counterpart and consistently outperforms LiDAR-only and multimodal baselines under both day and night conditions. The same policy is able to transfer to other SAT datasets with heterogeneous scan patterns, maintaining accurate and stable trajectories.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MeanFlow Transformers with Representation Autoencoders

Hu, Zheyuan, Lai, Chieh-Hsin, Wu, Ge, Mitsufuji, Yuki, Ermon, Stefano

原文摘要

MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used as a latent MF by leveraging the pre-trained Stable Diffusion variational autoencoder (SD-VAE) for high-dimensional data modeling. However, MF training remains computationally demanding and is often unstable. During inference, the SD-VAE decoder dominates the generation cost, and MF depends on complex guidance hyperparameters for class-conditional generation. In this work, we develop an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE), where a pre-trained vision encoder (e.g., DINO) provides semantically rich latents paired with a lightweight decoder. We observe that naive MF training in the RAE latent space suffers from severe gradient explosion. To stabilize and accelerate training, we adopt Consistency Mid-Training for trajectory-aware initialization and use a two-stage scheme: distillation from a pre-trained flow matching teacher to speed convergence and reduce variance, followed by an optional bootstrapping stage with a one-point velocity estimator to further reduce deviation from the oracle mean flow. This design removes the need for guidance, simplifies training configurations, and reduces computation in both training and sampling. Empirically, our method achieves a 1-step FID of 2.03, outperforming vanilla MF's 3.43, while reducing sampling GFLOPS by 38% and total training cost by 83% on ImageNet 256. We further scale our approach to ImageNet 512, achieving a competitive one-step FID of 3.23 with the lowest GFLOPS among all baselines.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity

Mai, Weijian, Nan, Mu, Zhu, Yu, Cao, Jiahang, Zhang, Rui, Dai, Yuqin, Song, Chunfeng, Luo, Andrew 等 9 位作者

原文摘要

Visual encoding and decoding models act as gateways to understanding the neural mechanisms underlying human visual perception. Typically, visual encoding models that predict brain activity from stimuli and decoding models that reproduce stimuli from brain activity are treated as distinct tasks, requiring separate models and training procedures. This separation is inefficient and fails to model the consistency between encoding and decoding processes. To address this limitation, we propose NeuroFlow, the first unified framework that jointly models visual encoding and decoding from neural activity within a single flow model. NeuroFlow introduces two key components: (i) NeuroVAE is designed as a variational backbone to model neural variability and establish a compact, semantically structured latent space for bidirectional modeling across visual and neural modalities. (ii) Cross-modal Flow Matching (XFM) bypasses the typical paradigm of noise-to-data diffusion guided by a specific modality condition, instead learning a reversibly consistent flow model between visual and neural latent distributions. For the first time, visual encoding and decoding are reformulated as a time-dependent, reversible process within a shared latent space for unified modeling. Empirical results demonstrate that NeuroFlow achieves superior overall performance in visual encoding and decoding tasks with higher computational efficiency compared to any isolated methods. We further analyze principal factors that steer the model toward encoding-decoding consistency and, through brain functional analyses, demonstrate that NeuroFlow captures consistent activation patterns underlying neural variability. NeuroFlow marks a major step toward unified visual encoding and decoding from neural activity, providing mechanistic insights that inform future bidirectional visual brain-computer interfaces. Code and project page are publicly available at https://michaelmaiii.github.io/NeuroFlow-S.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders

Walmer, Matthew, Suri, Saksham, Aggarwal, Anirud, Shrivastava, Abhinav

原文摘要

The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act as a shortcut to achieve dense features for a fraction of the cost by learning to map low-resolution features to high-resolution versions. While early works in this space used iterative upsampling approaches, more recent works have switched to cross-attention-based methods, which risk falling into the same efficiency scaling problems of the backbones they are upsampling. In this work, we demonstrate that iterative upsampling methods can still compete with cross-attention-based methods; moreover, they can achieve state-of-the-art performance with lower inference costs. We propose UPLiFT, an architecture for Universal Pixel-dense Lightweight Feature Transforms. We also propose an efficient Local Attender operator to overcome the limitations of prior iterative feature upsampling methods. This operator uses an alternative attentional pooling formulation defined fully locally. We show that our Local Attender allows UPLiFT to maintain stable features throughout upsampling, enabling state-of-the-art performance with lower inference costs than existing pixel-dense feature upsamplers. In addition, we apply UPLiFT to generative downstream tasks and show that it achieves competitive performance with state-of-the-art Coupled Flow Matching models for VAE feature upsampling. Altogether, UPLiFT offers a versatile and efficient approach to creating denser features.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition

Sun, Shengkai, Cheng, Zhiyong, Zhang, Zefan, Dong, Jianfeng, Li, Zhihui, Wang, Meng

原文摘要

Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised skeleton-based action recognition. However, existing state-of-the-art methods must predict an exceedingly large number of spatiotemporal patches, significantly prolonging training time. Besides, by treating all spatiotemporal regions equally during reconstruction, these models are distracted from learning the critical motion patterns that underlie action semantics. To address these challenges, we propose Adaptive Masked Reconstruction (AMR), a faster and stronger pre-training framework. We first decouple the decoder from the encoder, enabling flexible prediction of larger spatiotemporal patches and dramatically reducing reconstruction complexity. Given that larger patches contain more complex information, which is challenging to predict and consequently degrades performance, we accordingly introduce an adaptive guidance module. This module identifies regions of high motion informativeness, guiding the model to focus on the most discriminative parts of each patch and alleviating reconstruction difficulty. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that AMR not only accelerates pre-training substantially but also improves downstream recognition accuracy, surpassing current state-of-the-art approaches.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models

Ruiz-Ponce, Pablo, Escalera, Sergio, García-Rodríguez, José, Deng, Jiankang, Potamias, Rolandos Alexandros

原文摘要

Generating realistic human-human interactions is a challenging task that requires not only high-quality individual body and hand motions, but also coherent coordination among all interactants. Due to limitations in available data and increased learning complexity, previous methods tend to ignore hand motions, limiting the realism and expressivity of the interactions. Additionally, current diffusion-based approaches generate entire motion sequences simultaneously, limiting their ability to capture the reactive and adaptive nature of human interactions. To address these limitations, we introduce Interact2Ar, the first end-to-end text-conditioned autoregressive diffusion model for generating full-body, human-human interactions. Interact2Ar incorporates detailed hand kinematics through dedicated parallel branches, enabling high-fidelity full-body generation. Furthermore, we introduce an autoregressive pipeline coupled with a novel memory technique that facilitates adaptation to the inherent variability of human interactions using efficient large context windows. The adaptability of our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios. To validate the generated motions, we introduce a set of robust evaluators and extended metrics designed specifically for assessing full-body interactions. Through quantitative and qualitative experiments, we demonstrate the state-of-the-art performance of Interact2Ar.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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VL-RouterBench: A Benchmark for Vision-Language Model Routing

Huang, Zhehao, Lin, Baijiong, Zhang, Jingyuan, Wang, Jingying, Liu, Yuhang, Lu, Ning, Li, Tao, Huang, Xiaolin

原文摘要

Multi-model routing has evolved from an engineering technique into essential infrastructure, yet existing work lacks a systematic, reproducible benchmark for evaluating vision-language models (VLMs). We present VL-RouterBench to assess the overall capability of VLM routing systems systematically. The benchmark is grounded in raw inference and scoring logs from VLMs and constructs quality and cost matrices over sample-model pairs. In scale, VL-RouterBench covers 14 datasets across 3 task groups, totaling 30,540 samples, and includes 15 open-source models and 2 API models, yielding 519,180 sample-model pairs and a total input-output token volume of 34,494,977. The evaluation protocol jointly measures average accuracy, average cost, and throughput, and builds a ranking score from the harmonic mean of normalized cost and accuracy to enable comparison across router configurations and cost budgets. On this benchmark, we evaluate 10 routing methods and baselines and observe a significant routability gain, while the best current routers still show a clear gap to the ideal Oracle, indicating considerable room for improvement in router architecture through finer visual cues and modeling of textual structure. We open-source the complete data construction and evaluation toolchain to promote comparability, reproducibility, and practical deployment in multimodal routing research.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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