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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8aa0ad875d14db27122c5857a61b9eeeCVF · CVPR 2026

SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens

Zhang, Xiaoyan, Bai, Zechen, Wang, Haofan, Song, Yiren

原文摘要

Recent unified models such as Bagel demonstrate that paired image-edit data can effectively align multiple visual tasks within a single diffusion transformer. However, these models remain limited to single-condition inputs and lack the flexibility needed to synthesize results from multiple heterogeneous sources. We present SIGMA (Selective-Interleaved Generation with Multi-Attribute Tokens), a unified post-training framework that enables interleaved multi-condition generation within diffusion transformers. SIGMA introduces selective multi-attribute tokens, including style, content, subject and identity tokens, which allow the model to interpret and compose multiple visual conditions in an interleaved text-image sequence. Through post-training on the Bagel unified backbone with 700K interleaved examples, SIGMA supports compositional editing, selective attribute transfer and fine-grained multimodal alignment. Extensive experiments show that SIGMA improves controllability, cross-condition consistency and visual quality across diverse editing and generation tasks, with substantial gains over Bagel on compositional tasks. Code is available at https://github.com/auihund/SIGMA.

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:8ab27fccf733207730f5a9c8a12a0118CVF · CVPR 2026

MUSE: Harnessing Precise and Diverse Semantics for Few-Shot Whole Slide Image Classification

Xu, Jiahao, Huang, Sheng, Zhang, Xin, Nan, Zhixiong, Dong, Jiajun, Mu, Nankun

原文摘要

In computational pathology, few-shot whole slide image classification is primarily driven by the extreme scarcity of expert-labeled slides. Recent vision-language methods incorporate textual semantics generated by large language models, but treat these descriptions as static class-level priors that are shared across all samples and lack sample-wise refinement. This limits both the diversity and precision of visual-semantic alignment, hindering generalization under limited supervision. To overcome this, we propose the stochastic MUlti-view Semantic Enhancement (MUSE), a framework that first refines semantic precision via sample-wise adaptation and then enhances semantic richness through retrieval-augmented multi-view generation. Specifically, MUSE introduces Sample-wise Fine-grained Semantic Enhancement (SFSE), which yields a fine-grained semantic prior for each sample through MoE-based adaptive visual-semantic interaction. Guided by this prior, Stochastic Multi-view Model Optimization (SMMO) constructs an LLM-generated knowledge base of diverse pathological descriptions per class, then retrieves and stochastically integrates multiple matched textual views during training. These dynamically selected texts serve as enriched semantic supervisions to stochastically optimize the vision-language model, promoting robustness and mitigating overfitting. Experiments on three benchmark WSI datasets show that MUSE consistently outperforms existing vision-language baselines in few-shot settings, demonstrating that effective few-shot pathology learning requires not only richer semantic sources but also their active and sample-aware semantic optimization.

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:8aca39e19585576d9dbebc6782a3a3c9CVF · CVPR 2026

Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration

Kazerouni, Amirhossein, Suin, Maitreya, Aumentado-Armstrong, Tristan, Honari, Sina, Walia, Amanpreet, Mohomed, Iqbal, Derpanis, Konstantinos G., Taati, Babak 等 9 位作者

原文摘要

Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limits practical usability. Meanwhile, full-scene restorers often ignore degradation cues entirely, leading to underdetermined predictions and visual artifacts. In this work, we propose **Face2Scene**, a two-stage restoration framework that leverages the face as a perceptual oracle to estimate degradation and guide the restoration of the entire image. Given a degraded image and one or more identity references, we first apply a Ref-FR model to reconstruct high-quality facial details. From the restored-degraded face pair, we extract a face-derived degradation code that captures degradation attributes (e.g., noise, blur, compression), which is then transformed into multi-scale degradation-aware tokens. These tokens condition a diffusion model to restore the full scene in a single step, including the body and background. Extensive experiments demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods.

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:8ad7c05d646778e1e66d3dfad8cf6d03CVF · CVPR 2026

OnlinePG: Online Open-Vocabulary Panoptic Mapping with 3D Gaussian Splatting

Zhai, Hongjia, Zhang, Qi, Pan, Xiaokun, Zhang, Xiyu, Dong, Yitong, Zhang, Huaqi, Xu, Dan, Zhang, Guofeng

原文摘要

Open-vocabulary scene understanding with online panoptic mapping is essential for embodied applications to perceive and interact with environments. However, existing methods are predominantly offline or lack instance-level understanding, limiting their applicability to real-world robotic tasks. In this paper, we propose OnlinePG, a novel and effective system that integrates geometric reconstruction and open-vocabulary perception using 3D Gaussian Splatting in an online setting. Technically, to achieve online panoptic mapping, we employ an efficient local-to-global paradigm with a sliding window. To build local consistency map, we construct a 3D segment clustering graph that jointly leverages geometric and semantic cues, fusing inconsistent segments within sliding window into complete instances. Subsequently, to update the global map, we construct explicit spatial attribute grids for the local 3D Gaussian map and fuse them into the global map via robust bidirectional bipartite 3D Gaussian instance matching. Finally, we utilize the fused VLM features inside the 3D spatial attribute grids to achieve open-vocabulary scene understanding. Extensive experiments on widely used datasets demonstrate that our method achieves better performance among online approaches, while maintaining real-time efficiency.

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:8ae27c66cf2b73fdb8aec2ac82d9f64aCVF · CVPR 2026

ParallelVLM: Lossless Video-LLM Acceleration with Visual Alignment Aware Parallel Speculative Decoding

Kong, Quan, Shen, Yuhao, Ji, Yicheng, Li, Huan, Wang, Cong

原文摘要

Although current Video-LLMs achieve impressive performance in video understanding tasks, their autoregressive decoding efficiency remains constrained by the massive number of video tokens. Visual token pruning can partially ease this bottleneck, yet existing approaches still suffer from information loss and yield only modest acceleration in decoding. In this paper, we propose ParallelVLM, a training-free draft-then-verify speculative decoding framework that overcomes both mutual waiting and limited speedup-ratio problems between draft and target models in long-video settings. ParallelVLM features two parallelized stages that maximize hardware utilization and incorporates an Unbiased Verifier-Guided Pruning strategy to better align the draft and target models by eliminating the positional bias in attention-guided pruning. Extensive experiments demonstrate that ParallelVLM effectively expands the draft window by 1.6-1.8x with high accepted lengths, and accelerates various video understanding benchmarks by 3.36x on LLaVA-Onevision-72B and 2.42x on Qwen2.5-VL-32B compared with vanilla autoregressive decoding.

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:8ae86269e39ad694568c65aabb587434CVF · CVPR 2026

MonoVLM: Monocular 3D Visual Grounding with Vision Language Models

Qu, Huaizhi, Mahjoub, Hossein Nourkhiz, Tadiparthi, Vaishnav, Lee, Kwonjoon, Chen, Tianlong

原文摘要

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in instruction following and 2D visual understanding. However, state-of-the-art VLMs, including GPT-5, still struggle with 3D perception, particularly in tasks such as monocular 3D visual grounding. While specialized vision-only models excel in this domain, they often lack the rich semantic understanding inherent to VLMs. To bridge this gap, we propose \texttt MonoVLM , a novel triple-stage training framework that enables VLMs to perform accurate monocular 3D grounding. The core of our method is a progressive training process that uses Group Relative Policy Optimization (GRPO) to gradually teach the model to first localize the described object, then understand its 3D structure, and finally estimate the full 3D bounding box accurately. Comprehensive experiments show that \texttt MonoVLM models significantly outperform existing VLMs and even surpass the performance of specialized vision-only models. We validate our design via extensive comparisons and ablation studies.

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:8aea4e76c02a92c601dd5652969474e4CVF · CVPR 2026

Visual Grounding for Object Questions

Everaert, Martin Nicolas, Liu, Xiruo, Takeda, Hiroyuki, Bala, Raja, Yadav, Vivek, Narayanan, Vidya

原文摘要

Current visual grounding research remains limited for practical applications, because existing techniques primarily focus on direct visual queriesCurrent visual grounding research remains limited for practical applications, because existing tasks primarily focus on direct visual queries (e.g., "find the red car") or reading visible text (e.g., "what is the title of this book?"), rather than supporting general questions about objects (e.g., "how comfortable are these earbuds?"). We introduce the novel problem of Visual Grounding for Object Questions (VGOQ). Unlike previous tasks that ground only what is directly visible in images, VGOQ handles open-ended general questions about objects, including concepts such as ease and comfort of use, and aims to identify visual evidence or context that would support an answer. This unexplored problem has immediate practical value, particularly in designing and optimizing product imagery in e-commerce stores. As initial steps toward this task, we develop two automated data generation techniques, which serve to train a lightweight visual grounding model, and to evaluate visual grounding approaches on the resulting synthetic benchmarks, ABO-VGOQ and VizWiz-VGOQ. Our results provide initial evidence that VGOQ represents a meaningful research direction: current SoTA visual grounding performance decreases from 52% gIoU to 37% gIoU when questions are rephrased from visual questions (segmentation of the answer) to general object questions (segmentation of visual evidence). On our new benchmarks, our lightweight model outperforms prior models while being much smaller. Project page: https://martin-ev.github.io/vgoq.

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:8aed4a21af76b0f8ab0df64dbc5d2eb0CVF · CVPR 2026

SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models

Lian, Jiesong, Zhong, Ruizhe, Zhou, Zixiang, Mi, Xiaoyue, Hu, Long, Zhou, Yuan, Lu, Qinglin, Hao, Yixue 等 9 位作者

原文摘要

Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodological hurdles. Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise. Concurrently, the architectural design of VLM-based RMs, particularly their output mechanisms, remains underexplored. Furthermore, RM is susceptible to reward hacking in post-training. To mitigate these limitations, we propose SoliReward, a systematic framework for video RM training. Our framework first sources high-quality, cost-efficient data via single-item binary annotations, then constructs preference pairs using a cross-prompt pairing strategy. Architecturally, we employ a Hierarchical Progressive Query Attention mechanism to enhance feature aggregation. Finally, we introduce a modified BT loss that explicitly accommodates win-tie scenarios. This approach regularizes the RM's score distribution for positive samples, providing more nuanced preference signals to alleviate over-focus on a small number of top-scoring samples. Our approach is validated on benchmarks evaluating physical plausibility, subject deformity, and semantic alignment, demonstrating improvements in direct RM evaluation metrics and in the efficacy of post-training on video generation models. Code and benchmark are available at https://github.com/lian700/SoliReward.

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:8b03acf7ed6490605523c46839093fc2CVF · CVPR 2026

From Events to Clarity: The Event-Guided Diffusion Framework for Dehazing

Wang, Ling, Lu, Yunfan, Ma, Wenzong, Yao, Huizai, Li, Pengteng, Xiong, Hui

原文摘要

Clear imaging under hazy conditions is a critical task. Prior-based and neural methods have improved results. However, they operate on RGB frames, which suffer from limited dynamic range. Therefore, dehazing remains ill- posed and can erase structure and illumination details. To address this, we use event cameras for dehazing for the first time. Event cameras offer much higher HDR (120dB vs. 60dB) and microsecond latency, therefore they suit hazy scenes. In practice, transferring HDR cues from events to frames is hard because real paired data are scarce. To tackle this, we propose an event-guided diffusion model that utilizes the strong generative priors of diffusion mod- els to reconstruct clear images from hazy inputs by effec- tively transferring HDR information from events. Specifi- cally, we design an event-guided module that maps sparse HDR event features, e.g., edges, corners, into the diffusion latent space. This clear conditioning provides precise struc- tural guidance during generation, improves visual realism, and reduces semantic drift. For real-world evaluation, we collect a drone dataset in heavy haze (AQI = 341) with synchronized RGB and event sensors. Experiments on two benchmarks and our dataset achieve state-of-the-art results.

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:8b1646932b4bb789900b6d786ca01185CVF · CVPR 2026

Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance

Choi, June Suk, Lee, Kyungmin, Yu, Sihyun, Choi, Yisol, Shin, Jinwoo, Lee, Kimin

原文摘要

Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have considered fine-tuning pre-trained T2V models to support image-to-video (I2V) generation. However, such adaptation frequently suppresses motion dynamics of generated outputs, resulting in more static videos compared to their T2V counterparts. In this work, we analyze this phenomenon and identify that it stems from the premature exposure to high-frequency details in the input image, which biases the sampling process toward a shortcut trajectory that overfits to the static appearance of the reference image. To address this, we propose adaptive low-pass guidance (ALG), a simple training-free fix to the I2V model sampling procedure to generate more dynamic videos without compromising per-frame image quality. Specifically, ALG adaptively modulates the frequency content of the conditioning image by applying a low-pass filter at the early stage of denoising. Extensive experiments show ALG significantly improves the temporal dynamics of generated videos, while preserving or even improving image fidelity and text alignment. For instance, on the VBench test suite, ALG achieves a 33% average improvement across models in dynamic degree while maintaining the original video quality. For additional visualizations and source code, see the project page.

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:8b3322f6b6af90f9eff1f907e7250780CVF · CVPR 2026

CF-IPT: Cross-Modal Fusion Interactive Prompt Tuning of Vision-Language Pre-Trained Model for Multisource Remote Sensing Data Classification

Ji, Jinheng, Qu, Jiahui, Dong, Wenqian, Li, Yunsong

原文摘要

Fine-tuning Vision-Language Models (VLMs) trained on large-scale datasets of natural image-text pairs has demonstrated impressive performance for various downstream tasks. However, their fine-tuning for remote sensing (RS) tasks faces dual barriers: (1) Data-level barrier caused by the fundamental modality gap between natural imagery and RS data, and (2) Task-level barrier stemming from the requirement for multi-source interaction modeling capabilities. This paper proposes a Cross-modal Fusion Interactive Prompt Tuning (CF-IPT) method to fine-tune CLIP for multi-source RS image classification tasks. It aims to leverage the prompt learning framework to transfer the alignment target of the text branch shifts from natural images to multi-source RS images. Specifically, we design a Multi-source Interactive Fusion-guided Spectral-Spatial Prompt Generation (MFPG) module, which enables cross-modal feature interaction to generate a prompt matrix that preserves the original spectral and spatial information while performing adaptive multi-scale fusion to address the multi-source image adaptation problem. Subsequently, a Spectral-Spatial Prompt-guided Visual-Text Prompt Interaction (V-TPI) Strategy is proposed, which leverages spectral-spatial prompt matrices to guide visual-textual prompt interaction and inject RS-specific information into both branches of CLIP, ultimately enabling multi-source RS image-text representation alignment. The proposed approach performs the downstream task of multi-source RS image classification with merely 0.76% of CLIP's parameters. It is evaluated on several widely used datasets, demonstrating the effectiveness of the proposed approach.

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:8b3d28792974106649afdd8dcf505a29CVF · CVPR 2026

Generalizable Structure-Aware Keypoint Correspondence for Category-Unified 3D Single Object Tracking

Xiao, Jie, Ma, Yinchao, Tang, Yuyang, Yang, Dengqing, Yang, Jianpeng, Zhou, Xu, Li, Qiao, Yang, Wenfei 等 9 位作者

原文摘要

3D single object tracking (SOT) in point clouds is essential for real-world 3D perception, yet it remains challenging due to data sparsity and large variations in scale and structure across diverse object categories. Most existing methods rely on a category-specific paradigm that trains separate models for each class, severely limiting scalability and generalization in real deployment. Extending these methods to a single model capable of tracking diverse object categories proves inadequate, as the significant variations across categories make it difficult to establish reliable geometric correspondences without category-specific priors. To overcome these limitations, we propose a Unified Structural KeyPoint Tracker (UniKPT), a novel structure-aware and generalizable framework for category-unified 3D point cloud tracking. UniKPT comprises three key modules: (1) an adaptive keypoint extractor that produces scale-aware and semantically meaningful keypoints; (2) a progressive correspondence aligner that establishes robust cross-frame geometric associations; and (3) a confidence-aware structural localization module that suppresses unreliable matches and leverages fine-grained structural relationships for precise 3D localization. Extensive experiments on the nuScenes and KITTI benchmarks show that UniKPT achieves new state-of-the-art performance in category-unified 3D SOT. On the challenging nuScenes dataset, our unified model further surpasses category-specific state-of-the-art trackers by 4.37% in Success and 5.16% in Precision.

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:8b4aa1c12dd918a2bd610181657a4552CVF · CVPR 2026

Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos

Strong, Matthew, Chang, Wei-Jer, Herau, Quentin, Yang, Jiezhi, Hu, Yihan, Peng, Chensheng, Zhan, Wei

原文摘要

Ego-centric driving videos available online provide an abundant source of visual data for autonomous driving, yet their lack of annotations makes it difficult to learn representations that capture both semantic structure and 3D geometry. Recent advances in large feedforward spatial models demonstrate that point maps and ego-motion can be inferred in a single forward pass, suggesting a promising direction for scalable driving perception. We therefore propose a label-free, teacher-guided framework for learning autonomous driving representations directly from unposed videos. Unlike prior self-supervised approaches that focus primarily on frame-to-frame consistency, we posit that safe and reactive driving depends critically on temporal context. To this end, we leverage a feedforward architecture equipped with a lightweight autoregressive module, trained using multi-modal supervisory signals that guide the model to jointly predict current and future point maps, camera poses, semantic layouts, and motion masks. Multi-modal teachers provide sequence-level pseudo-supervision, enabling LFG to learn a unified pseudo-4D representation from raw YouTube videos without poses, labels, or LiDAR. The resulting encoder not only transfers effectively to downstream autonomous driving planning on the NAVSIM benchmark, surpassing multi-camera and LiDAR baselines with only a single monocular camera, but also yields strong performance when evaluated on a range of semantic, geometric, and motion prediction tasks. These geometry- and motion-aware features position LFG as a compelling video-centric foundation model for autonomous driving.

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:8b503d83c15074f8a4e04aa385b9346bCVF · CVPR 2026

MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent

Fu, Yuxia, Zhang, Zhizhen, Zhang, Yuqi, Wang, Zijian, Huang, Zi, Luo, Yadan

原文摘要

Recent Vision-Language-Action (VLA) models reformulate vision-language models by tuning them with millions of robotic demonstrations. While they perform well when fine-tuned for a single embodiment or task family, extending them to multi-skill settings remains challenging: directly merging VLA experts trained on different tasks results in near-zero success rates. This raises a fundamental question: what prevents VLAs from mastering multiple skills within one model? With an empirical decomposition of learnable parameters during VLA fine-tuning, we identify two key sources of non-mergeability:(1) Finetuning drives LoRA adapters in the VLM backbone toward divergent, task-specific directions beyond the capacity of existing merging methods to unify.(2) Action experts develop inter-block dependencies through self-attention feedback, causing task information to spread across layers and preventing modular recombination.To address these challenges, we present MergeVLA, a merging-oriented VLA architecture that preserves mergeability by design.MergeVLA introduces sparsely activated LoRA adapters via task masks to retain consistent parameters and reduce irreconcilable conflicts in the VLM.Its action expert replaces self-attention with cross-attention-only blocks to keep specialization localized and composable.When the task is unknown, it uses a test-time task router to adaptively select the appropriate task mask and expert head from the initial observation, enabling unsupervised task inference.Across LIBERO, LIBERO-Plus, RoboTwin, and multi-task experiments on the real SO101 robotic arm, MergeVLA achieves performance comparable to or even exceeding individually finetuned experts, demonstrating robust generalization across tasks, embodiments, and environments. Project page: https://mergevla.github.io/

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:8b6734804a3ba5eb176a9e9fce26895cCVF · CVPR 2026

FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation

Wang, Hanxiao, Guo, Yuan-Chen, Liu, Ying-Tian, Zou, Zi-Xin, Zhang, Biao, Quan, Weize, Liang, Ding, Cao, Yan-Pei 等 9 位作者

原文摘要

Autoregressive models for 3D mesh generation suffer from a fundamental limitation: they flatten meshes into long vertex-coordinate sequences. This results in prohibitive computational costs, hindering the efficient synthesis of high-fidelity geometry. We argue this bottleneck stems from operating at the wrong semantic level. We introduce FACE, a novel Autoregressive Autoencoder (ARAE) framework that reconceptualizes the task by generating meshes at the face level. Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token. This simple yet powerful design reduces the sequence length by a factor of nine, leading to an unprecedented compression ratio of 0.11, halving the previous state-of-the-art. This dramatic efficiency gain does not compromise quality; by pairing our face-level decoder with a powerful VecSet encoder, FACE achieves state-of-the-art reconstruction quality on standard benchmarks. The versatility of the learned latent space is further demonstrated by training a latent diffusion model that achieves high-fidelity, single-image-to-mesh generation. FACE provides a simple, scalable, and powerful paradigm that lowers the barrier to high-quality structured 3D content creation.

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:8b686a140a958604aae72d4812436755CVF · CVPR 2026

LAOF: Robust Latent Action Learning with Optical Flow Constraints

Bu, Xizhou, Lyu, Jiexi, Sun, Fulei, Yang, Ruichen, Ma, Zhiqiang, Li, Wei

原文摘要

Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant distractors. Although incorporating action supervision can alleviate these distractions, its effectiveness is restricted by the scarcity of available action labels. Optical flow represents pixel-level motion between consecutive frames, naturally suppressing background elements and emphasizing moving objects. Motivated by this, we propose robust Latent Action learning with Optical Flow constraints (LAOF), a pseudo-supervised framework that leverages the agent's optical flow as an action-driven signal to learn latent action representations robust to distractors. Experimental results show that the latent representations learned by LAOF outperform existing methods on downstream imitation learning and reinforcement learning tasks. This superior performance arises from optical flow constraints, which substantially stabilize training and improve the quality of latent representations under extremely label-scarce conditions, while remaining effective as the proportion of action labels increases to 10%. Importantly, even without action supervision, LAOF matches or surpasses action-supervised methods trained with 1% of action labels.

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:8b6ed233cf278d1c34958cafd590b1f0CVF · CVPR 2026

Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack

Aydin, M. Kerem, Hung, Yi-Chun, Pytlarz, Jaclyn, Guo, Qi, Alexander, Emma

原文摘要

Hyperspectral cameras rely on spectral filters, dispersive optics, or coded apertures, which reduce light throughput and increase hardware complexity. These systems face harsh trade-offs between spatial, spectral, and temporal resolution in inherently low-photon conditions. Computational imaging systems break through these trade-offs with compressive sensing, but have typically required complex optics and/or extensive computation. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that achieves state-of-the-art hyperspectral imaging using only two off-the-shelf lenses, a grayscale sensor, and less than one second of reconstruction time. By capturing a chromatically-aberrated focal stack that preserves nearly all incident light, and reconstructing it with a fast physics-based iterative algorithm, SfD delivers sharp, accurate hyperspectral images. The combination of photon efficiency, optical simplicity, and physical interpretability makes SfD a promising solution for fast, compact, and interpretable hyperspectral imaging.

cs.CV

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

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CLIP Is Shortsighted: Paying Attention Beyond the First Sentence

Lavoie, Marc-Antoine, Mahmoud, Anas, Zaimi, Aldo, Tchango, Arsene Fansi, Waslander, Steven L.

原文摘要

CLIP models learn transferable multi-modal features via image-text contrastive learning on internet-scale data. They are widely used in zero-shot classification, multi-modal retrieval, text-to-image diffusion, and as image encoders in large vision-language models. However, CLIP's pretraining is dominated by images paired with short captions, biasing the model toward encoding simple descriptions of salient objects and leading to coarse alignment on complex scenes and dense descriptions. While recent work mitigates this by fine-tuning on small-scale long-caption datasets, we identify an important common bias: both human- and LLM-generated long captions typically begin with a one-sentence summary followed by a detailed description. We show that this acts as a shortcut during training, concentrating attention on the opening sentence and early tokens and weakening alignment over the rest of the caption. To resolve this, we introduce DeBias-CLIP, which removes the summary sentence during training and applies sentence sub-sampling and text token padding to distribute supervision across all token positions. DeBias-CLIP achieves state-of-the-art long-text retrieval, improves short-text retrieval, and is less sensitive to sentence order permutations. It is a drop-in replacement for Long-CLIP with no additional trainable parameters.

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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GeoSANE: Learning Geospatial Representations from Models, Not Data

Hanna, Joëlle, Falk, Damian, Yu, Stella X., Borth, Damian

原文摘要

Recent advances in remote sensing have led to an increase in the number of available foundation models; each trained on different modalities, datasets, and objectives, yet capturing only part of the vast geospatial knowledge landscape. While these models show strong results within their respective domains, their capabilities remain complementary rather than unified. Therefore, instead of choosing one model over another, we aim to combine their strengths into a single shared representation.We introduce GeoSANE, a geospatial model foundry that learns a unified neural representation from the weights of existing foundation models and task-specific models, able to generate novel neural networks weights on-demand. Given a target architecture, GeoSANE generates weights ready for finetuning for classification, segmentation, and detection tasks across multiple modalities.Models generated by GeoSANE consistently outperform their counterparts trained from scratch, match or surpass state-of-the-art remote sensing foundation models, and outperform models obtained through pruning or knowledge distillation when generating lightweight networks. Evaluations across ten diverse datasets and on GEO-Bench confirm its strong generalization capabilities.By shifting from pre-training to weight generation, GeoSANE introduces a new framework for unifying and transferring geospatial knowledge across models and tasks. Code is available at \href https://hsg-aiml.github.io/GeoSANE/ hsg-aiml.github.io/GeoSANE/ .

cs.CV

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

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EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing

Fu, Yang, Zheng, Yike, Dai, Ziyun, Ding, Henghui

原文摘要

Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds. Recent diffusion-based video inpainting and object removal methods can remove the objects but often struggle to erase these effects and to synthesize coherent backgrounds. Beyond method limitations, progress is further hampered by the lack of a comprehensive dataset that systematically captures common object effects across varied environments for training and evaluation. To address this, we introduce **VOR** (**V**ideo **O**bject **R**emoval), a large-scale dataset that provides diverse paired videos, each consisting of one video where the target object is present with its effects and a counterpart where the object and effects are absent, with corresponding object masks. VOR contains 60k high-quality video pairs from captured and synthetic sources, covers five effects types, and spans a wide range of object categories as well as complex, dynamic multi-object scenes. Building on VOR, we propose ***EffectErase***, an effect-aware video object removal method that treats video object insertion as the inverse auxiliary task within a reciprocal learning scheme. The model includes task-aware region guidance that focuses learning on affected areas and enables flexible task switching. Then, an insertion-removal consistency objective that encourages complementary behaviors and shared localization of effect regions and structural cues. Trained on VOR, EffectErase achieves superior performance in extensive experiments, delivering high-quality video object effect erasing across diverse scenarios.

cs.CV

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

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See It, Say It, Sorted: An Iterative Training-Free Framework for Visually-Grounded Multimodal Reasoning in LVLMs

Zhang, Yongchang, Ma, Oliver, Liu, Tianyi, Zhou, Guangquan, Chen, Yang

原文摘要

Recent large vision-language models (LVLMs) have demonstrated impressive reasoning ability by generating long chain-of-thought (CoT) responses. However, CoT reasoning in multimodal contexts is highly vulnerable to visual hallucination propagation: once an intermediate reasoning step becomes inconsistent with the visual evidence, subsequent steps-even if logically valid-can still lead to incorrect final answers. Existing solutions attempt to mitigate this issue by training models to "think with images" via reinforcement learning (RL). While effective, these methods are costly, model-specific, and difficult to generalize across architectures. Differently, we present a lightweight method that bypasses RL training and provides an iterative, training-free, plug-and-play framework for visually-grounded multimodal reasoning. Our key idea is to supervise each reasoning step at test time with visual evidence, ensuring that every decoded token is justified by corresponding visual cues. Concretely, we construct a textual visual-evidence pool that guides the model's reasoning generation. When existing evidence is insufficient, a visual decider module dynamically extracts additional relevant evidence from the image based on the ongoing reasoning context, expanding the pool until the model achieves sufficient visual certainty to terminate reasoning and produce the final answer. Extensive experiments on multiple LVLM backbones and benchmarks demonstrate the effectiveness of our approach. Our method achieves 16.5%-29.5% improvements on TreeBench and 13.7% RH-AUC gains on RH-Bench, substantially reducing hallucination rates while improving reasoning accuracy without additional training.

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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Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation

Lin, Xin, Song, Meixi, Zhang, Dizhe, Lu, Wenxuan, Li, Haodong, Du, Bo, Yang, Ming-Hsuan, Nguyen, Truong 等 9 位作者

原文摘要

In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: https://insta360-research-team.github.io/DAP_website/

cs.CV

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

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SO-Bench: A Structural Output Evaluation of Multimodal LLM

Feng, Di, Ma, Kaixin, Nan, Feng, Chen, Haofeng, Zhai, Bohan, Griffiths, David, Gao, Mingfei, Gan, Zhe 等 12 位作者

原文摘要

Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to pre-defined data schemas. Despite recent progress in structured generation in textual domain, there is still no benchmark that systematically evaluates schema-grounded information extraction and reasoning over visual inputs. In this work, we conduct a comprehensive study of visual structural output capabilities for MLLMs with our carefully designed SO-BENCH benchmark. Covering four visual domains, including UI screens, natural images, documents, and charts, SO-BENCH is built from over 6.5K diverse JSON schemas and 1.8K curated image-schema pairs with human-verified quality. Benchmarking experiments on open-sourced andfrontier proprietary models reveal persistent gaps in predicting accurate, schema compliant outputs, highlighting the need for better multimodal structured reasoning. Beyond benchmarking, we further conduct training experiments to largely improve the model's structured output capability. We make the benchmark publicly available at https://github.com/apple/ml-sobench.

cs.CV · cs.CL · cs.AI

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

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Probabilistic Discrepancy Learning for Roadside LiDAR Scene Completion

Wu, Xiaogang, Hu, Jinchao, Wang, Zixian, Liu, Dun, Cheng, BoXiang, Wu, Yiqiang

原文摘要

We propose a probabilistic discrepancy learning approach for roadside LiDAR scene completion (PDL). Conventional methods focus on object-level completion and scene completion from ego-vehicle viewpoint. These methods struggle to cope with long-term or severe occlusions caused by roadside sensors with fixed viewpoints. To address this issue, we compensate for occlusion roadside point clouds by introducing external visual information. Specifically, the PDL is mainly divided into probabilistic pose discrepancy minimization and scene discrepancy learning. We employ probabilistic pose discrepancy minimization to correct noisy poses from vision-based detectors, while utilizing a diffusion model within scene discrepancy learning for robust full-scene completion. Furthermore, we design regional and global sampling discrepancy learning losses to achieve robust and efficient training. We conducted extensive experiments on the V2X-Seq and TUMTraf-V2X roadside datasets. Results demonstrate that PDL achieves state-of-the-art performance, with average reductions of 14.5% in chamfer distance (CD) and 6% in 3D Jensen Shannon divergence (JSD) compared to existing methods.

cs.CV

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

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MSRL: Scaling Generative Multimodal Reward Modeling via Multi-Stage Reinforcement Learning

Wang, Chenglong, Huo, Yifu, Gan, Yang, He, Qiaozhi, Meng, Qi, Li, Bei, Wang, Yan, Liu, Junfu 等 11 位作者

原文摘要

Recent advances in multimodal reward modeling have been largely driven by a paradigm shift from discriminative to generative approaches. Building on this progress, recent studies have further employed reinforcement learning with verifiable rewards (RLVR) to enhance multimodal reward models (MRMs). Despite their success, RLVR-based training typically depends on labeled multimodal preference data, which are costly and labor-intensive to obtain, making it difficult to scale the training of MRMs. To overcome this limitation, we propose a Multi-Stage Reinforcement Learning (MSRL) approach, which can achieve scalable reinforcement learning for MRMs with limited multimodal data. MSRL redefines the conventional RLVR-based training paradigm by first learning a generalizable reward reasoning capability from large-scale textual preference data and then progressively transferring this capability to multimodal tasks through caption-based and fully multimodal reinforcement learning stages. Furthermore, we introduce a cross-modal knowledge distillation approach to improve preference generalization within MSRL. Extensive experiments demonstrate that MSRL effectively scales the RLVR-based training of generative MRMs and substantially improves their performance across both visual understanding and visual generation tasks (e.g., 68.5%-74.8% on VLReward Bench, 69.2%-75.4% on GenAI-Bench), without requiring additional multimodal preference annotations.

cs.CV

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

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3D-Object Perception Transformer (3PT)

Kalra, Agastya, Salzmann, Tim, Stoppi, Guy, Marin, Dmitrii, Agarwal, Rishav, Taamazyan, Vage, Bokeloh, Martin, Hinterstoisser, Stefan 等 13 位作者

原文摘要

Current approaches to zero-shot 3D-object perception typically rely on ensembles of frozen foundation models. This limits deep object understanding and cross-domain generalization, making performance inadequate for real-world deployment. The 3D-Object Perception Transformer (3PT) addresses this limitation by unifying detection, segmentation, and 6DoF pose estimation in a single framework, directly trained for 3D-object perception. Based on two large-scale trained transformers that specialize in 2D and 3D object-centric scene understanding respectively, 3PT continuously refines its object representations without depth input, enhancing 3D understanding by incorporating multi-view information. 3PT is the state-of-the-art for detection and pose estimation on the BOP benchmarks, often achieving double digit improvements, in many cases, outperforming non-zero-shot methods, and winning 7 of 11 tracks in the BOP-2025 challenge. 3PT surpasses task-specialized models for detection and pose estimation, often achieving double-digit percentage improvements on the diverse BOP-benchmarks, and in some cases outperforming non zero-shot methods. It also ranked first in 7 of 11 tracks at the BOP Challenge 2025. 3PT's high-accuracy and reliability is well-suited for practical industrial robotics applications such as bin picking and precise insertion. Project Page can be found at https://www.intrinsic.ai/publications/3pt-cvpr2026.

cs.CV

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

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COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs

Guo, Peizheng, Wang, Jingyao, Qiang, Wenwen, Zhou, Jiahuan, Zheng, Changwen, Hua, Gang

原文摘要

Despite Multimodal Large Language Models (MLLMs) having shown impressive capabilities, they may suffer from hallucinations. Empirically, we find that MLLMs attend disproportionately to task-irrelevant background regions compared with text-only LLMs, implying spurious background-answer correlations. We claim and analyze that (i) outcome-based rewards can be an important factor leading to spurious correlations, and (ii) spurious correlations can be an important factor leading to hallucinations. Based on these results, we propose Causal-Oriented Policy Optimization (COPO) to mitigate these spurious correlations, thus addressing the issue of hallucinations. It imposes token-level sufficiency and necessity constraints to measure each inference token's causal contribution, thus ensuring correct and evidence-grounded output. Specifically, we first evaluate each token's causal contribution via a newly proposed causal completeness reward. This reward is then used to construct a causally informed advantage function within the GRPO optimization framework, encouraging the model to focus on tokens that are causally sufficient and necessary for accurate generation. Experimental results across various benchmarks demonstrate the advantages of COPO.

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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MuM: Multi-View Masked Image Modeling for 3D Vision

Nordström, David, Edstedt, Johan, Kahl, Fredrik, Bökman, Georg

原文摘要

Self-supervised learning on images seeks to extract meaningful visual representations from unlabeled data. When scaled to large datasets, this paradigm has achieved state-of-the-art performance and the resulting trained models such as DINOv3 have seen widespread adoption. However, most prior efforts are optimized for semantic understanding rather than geometric reasoning. One important exception is Cross-View Completion, CroCo, which is a form of masked autoencoding (MAE) tailored for 3D understanding. In this work, we continue on the path proposed by CroCo and focus on learning features tailored for 3D vision. In a nutshell, we extend MAE to arbitrarily many views of the same scene. By uniformly masking all views and employing a lightweight decoder with inter-frame attention, our approach is inherently simpler and more scalable than CroCo. We evaluate the resulting model, MuM, extensively on downstream tasks including feedforward reconstruction, dense image matching and relative pose estimation, finding that it outperforms the state-of-the-art visual encoders DINOv3 and CroCo v2.

cs.CV

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

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SignPR: A Progressive Vector-Quantized Diffusion Framework for Sign Language Production

Liu, Xiao, Gan, Shiwei, Yin, Yafeng, Guo, Bowen, Jiang, Zhiwei, Meng, Shunmei, Xie, Lei, Lu, Sanglu

原文摘要

Sign language production aims to generate sign sequences from spoken language, where the generation of sign pose sequences from text is often treated as a significant task. However, due to the differences in grammatical rules and modalities between sign language pose sequences and spoken language text, it is rather challenging to convert text into sign poses (i.e., Text2Pose), while maintaining semantic consistency, motion accuracy and temporal coherence.In this paper, we focus on the Text2Pose task, and propose SignPR, a progressive diffusion framework that jointly models the structural and temporal properties of signing. Structurally, we perform progressive structural refinement: a structural VQVAE encodes each frame into semantic-aware and region-based discrete representations; the diffusion process first produces semantically consistent poses and then progressively refines motion details under text and semantic conditioning. Temporally, we introduce block-wise causal diffusion, which progressively enforces temporal coherence and enables iterative refinement to earlier generated segments, yielding smoother transitions and reduced jitter. Extensive experiments on widely used datasets demonstrate that SignPR achieves superior results compared with prior T2P methods across multiple metrics, producing pose sequences that are semantically faithful, motion-accurate, and temporally coherent.

cs.CV

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

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WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing

Pan, Kaihang, Chen, Weile, Qiu, Haiyi, Yu, Qifan, Bu, Wendong, Wang, Zehan, Zhu, Yun, Li, Juncheng 等 9 位作者

原文摘要

Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation of cognition- and creativity-informed image editing, featuring deep task depth and broad knowledge breadth. Drawing an analogy to human cognitive creation, WiseEdit decomposes image editing into three cascaded steps--Awareness, Interpretation, and Imagination--each corresponding to a task that poses a challenge for models to complete at the specific step. It also encompasses complex tasks, where none of the three steps can be finished easily. Furthermore, WiseEdit incorporates three fundamental types of knowledge: Declarative, Procedural, and Metacognitive knowledge. Ultimately, WiseEdit comprises 1,220 test cases, objectively revealing the limitations of SoTA image editing models in knowledge-based cognitive reasoning and creative composition capabilities.

cs.CV

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

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