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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2ba1609d54669d846323c3bd7e306b2bCVF · CVPR 2026

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models

Mai, Zheda, Chowdhury, Arpita, Wang, Zihe, Jeon, Sooyoung, Wang, Lemeng, Hou, Jiacheng, Kil, Jihyung, Chao, Wei-Lun

原文摘要

The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as general-purpose heads, followed by evaluation on broad Visual Question Answering (VQA) benchmarks. However, this protocol has two key blind spots: (i) Instruction tuning data may not align with VQA test distributions, meaning a wrong prediction can stem from such data mismatch rather than VFMs' visual shortcomings; (ii) VQA benchmarks often require multiple visual abilities in a single question, making it difficult to determine whether errors arise from the lack of all required abilities or just one key ability. To address these gaps, we introduce AVA-Bench, the first benchmark that explicitly disentangles 14 Atomic Visual Abilities (AVAs), foundational skills such as localization, depth estimation, and spatial understanding, which collectively support complex visual reasoning tasks. By decoupling AVAs and matching training and test distributions within each, AVA-Bench pinpoints exactly where a VFM excels or falters. Applying AVA-Bench to leading VFMs thus reveals distinctive "ability fingerprints," turning VFM selection from educated guesswork into principled engineering. Notably, we find that a 0.5B LLM yields similar VFM rankings as a 7B LLM while cutting GPU hours by 8x, enabling more efficient evaluation. By offering a comprehensive and transparent benchmark, we hope AVA-Bench lays the foundation for the next generation of VFMs.

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:2bb024e72ed17f81d64845e6d36e4894CVF · CVPR 2026

MedLoc-R1: Performance-Aware Curriculum Reward Scheduling for GRPO-Based Medical Visual Grounding

Yang, Guangjing, Qin, Ziyuan, Zhang, Chaoran, Du, Chenlin, Wang, Jinglin, Sun, Wanran, Zhang, Zhenyu, Ji, Bing 等 9 位作者

原文摘要

Medical visual grounding serves as a crucial foundation for fine-grained multimodal reasoning and interpretable clinical decision support. Despite recent advances in reinforcement learning (RL) for grounding tasks, existing approaches such as Group Relative Policy Optimization (GRPO) suffer from severe reward sparsity when directly applied to medical images, primarily due to the inherent difficulty of localizing small or ambiguous regions of interest, which is further exacerbated by the rigid and suboptimal nature of fixed IoU-based reward schemes in RL. This leads to vanishing policy gradients and stagnated optimization, particularly during early training. To address this challenge, we propose MedLoc-R1, a performance-aware reward scheduling framework that progressively tightens the reward criterion in accordance with model readiness. MedLoc-R1 introduces a sliding-window performance tracker and a multi-condition update rule that automatically adjust the reward schedule from dense, easily obtainable signals to stricter, fine-grained localization requirements, while preserving the favorable properties of GRPO without introducing auxiliary networks or additional gradient paths. Experiments on three medical visual grounding benchmarks demonstrate that MedLoc-R1 consistently improves both localization accuracy and training stability over GRPO-based baselines. Our framework offers a general, lightweight, and effective solution for RL-based grounding in high-stakes medical applications. Code and checkpoints will be released after acceptance.

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:2bbc39218229fbb5d22604c95488f587CVF · CVPR 2026

Life-IQA: Boosting Blind Image Quality Assessment through GCN-enhanced Layer Interaction and MoE-based Feature Decoupling

Tang, Long, Duan, Huiyu, Zheng, Guoquan, Zhang, Jianbo, Hao, Jie, Yuan, Liang

原文摘要

Blind image quality assessment (BIQA) plays a crucial role in evaluating and optimizing visual experience. Most existing BIQA approaches fuse shallow and deep features extracted from backbone networks, while overlooking the unequal contributions to quality prediction. Moreover, while various vision encoder backbones are widely adopted in BIQA, the effective quality decoding architectures remain underexplored. To address these limitations, this paper investigates the contributions of shallow and deep features to BIQA, and proposes a effective quality feature decoding framework via GCN-enhanced \underline l ayer\underline i nteraction and MoE-based \underline f eature d\underline e coupling, termed (Life-IQA). Specifically, the GCN-enhanced layer interaction module utilizes the GCN-enhanced deepest-layer features as query and the penultimate-layer features as key, value, then performs cross-attention to achieve feature interaction. Moreover, a MoE-based feature decoupling module is proposed to decouple fused representations though different experts specialized for specific distortion types or quality dimensions. Extensive experiments demonstrate that Life-IQA shows more favorable balance between accuracy and cost than a vanilla Transformer decoder and achieves state-of-the-art performance on multiple BIQA benchmarks.

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:2bc2aab25ea6c263dd958aa3b21fdadaCVF · CVPR 2026

EgoProx: Evaluating MLLMs on Egocentric 3D Proximity Reasoning Across a Cognitive Hierarchy

Li, Jinzhao, Chen, Yinuo, Piao, Dongxu, Pan, Panwang, Yu, Yifan, Wang, Dong, Yan, Honglei, Yue, Liang 等 12 位作者

原文摘要

Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life. Whether multimodal large language models (MLLMs) can perform such embodied 3D reasoning remains unclear. To this end, we introduce EgoProx, a benchmark for egocentric 3D proximity reasoning. We organize our tasks along a cognitive chain, covering intention, exploration, exploitation, and chain-of-actions reasoning. We also design an agent based data engine that produces diverse and consistent QA pairs at scale. We benchmark prevailing MLLMs on EgoProx and conduct additional analyses with dataset specific and task specific instruction tuning. We observe large cross-domain gains, indicating that current MLLMs contain some spatial knowledge; however, they still struggle to effectively leverage it for spatial reasoning VQA.

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:2bcf15c252af06ea48bb66a29797deb3CVF · CVPR 2026

SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection

Wang, Yifan, Zhao, Yian, Pu, Fanqi, Yang, Xiaochen, Tang, Yang, Chen, Xi, Yang, Wenming

原文摘要

Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm, which employs multiple branches to estimate geometric center, depth, dimensions, and rotation angle separately.Although this decoupling strategy simplifies the learning process, it inherently ignores the geometric collaborative constraints between different attributes, resulting in the lack of geometric consistency prior, thereby leading to suboptimal performance. To address this issue, we propose novel Spatial-Projection Alignment (SPAN) with two pivotal components: (i). Spatial Point Alignment enforces an explicit global spatial constraint between the predicted and ground-truth 3D bounding boxes, thereby rectifying spatial drift caused by decoupled attribute regression. (ii). 3D-2D Projection Alignment ensures that the projected 3D box is aligned tightly within its corresponding 2D detection bounding box on the image plane, mitigating projection misalignment overlooked in previous works. To ensure training stability, we further introduce a Hierarchical Task Learning strategy that progressively incorporates spatial-projection alignment as 3D attribute predictions refine, preventing early stage error propagation across attributes. Extensive experiments demonstrate that the proposed method can be easily integrated into any established monocular 3D detector and delivers significant performance improvements.

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:2bd1af2c15ecd6e3539604191a93fb1bCVF · CVPR 2026

Cut to the Chase: Training-free Multimodal Summarization via Chain-of-Events

You, Xiaoxing, Huang, Qiang, Li, Lingyu, Chang, Xiaojun, Yu, Jun

原文摘要

Multimodal Summarization (MMS) aims to generate concise textual summaries by understanding and integrating information across videos, transcripts, and images. However, existing approaches still suffer from three main challenges: (1) reliance on domain-specific supervision, (2) implicit fusion with weak cross-modal grounding, and (3) flat temporal modeling without event transitions. To address these issues, we introduce **CoE**, a training-free MMS framework that performs structured reasoning through a **Chain-of-Events** guided by a Hierarchical Event Graph (HEG). The HEG encodes textual semantics into an explicit event hierarchy that scaffolds cross-modal grounding and temporal reasoning. Guided by this structure, **CoE** localizes key visual cues, models event evolution and causal transitions, and refines outputs via lightweight style adaptation for domain alignment. Extensive experiments on eight diverse datasets demonstrate that **CoE** consistently outperforms state-of-the-art video CoT baselines, achieving average gains of **+3.04 ROUGE**, **+9.51 CIDEr**, and **+1.88 BERTScore**, highlighting its robustness, interpretability, and cross-domain generalization. Our code is available at https://github.com/youxiaoxing/CoE.

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:2bd5e17f6ee4698279d0e04aabb8fcaaCVF · CVPR 2026

LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs

Bozorgtabar, Behzad, Mahapatra, Dwarikanath, Roy, Sudipta, Naseer, Muzammal, Razzak, Imran, Ge, Zongyuan

原文摘要

Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging, but their reliability under domain shift hinges on calibrated uncertainty with guarantees. Split conformal prediction (SCP) offers finite-sample coverage, yet prediction sets often become large (low efficiency) and class-wise coverage unbalanced--high class-conditioned coverage gap (CCV), especially in few-shot, imbalanced regimes; moreover, naively adapting to calibration labels breaks exchangeability and voids guarantees. We propose LATA (Laplacian-Assisted Transductive Adaptation), a training- and label-free refinement that operates on the joint calibration and test pool by smoothing zero-shot probabilities over an image-image kNN graph using a small number of CCCP mean-field updates, preserving SCP validity via a deterministic transform. We further introduce a failure-aware conformal score that plugs into the vision-language uncertainty (ViLU) framework, providing instance-level difficulty and label plausibility to improve prediction set efficiency and class-wise balance at fixed coverage. LATA is black-box (no VLM updates), compute-light (windowed transduction, no backprop), and includes an optional prior knob that can run strictly label-free or, if desired, in a label-informed variant using calibration marginals once. Across three medical VLMs and nine downstream tasks, LATA consistently reduces set size and CCV while matching or tightening target coverage, outperforming prior transductive baselines and narrowing the gap to label-using methods, while using far less compute. Comprehensive ablations and qualitative analyses show that LATA sharpens zero-shot predictions without compromising exchangeability.

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:2bef17ab105607a1cf54a492f823489fCVF · CVPR 2026

PercHead: Perceptual Head Model for Single-Image 3D Head Reconstruction & Editing

Oroz, Antonio, Nießner, Matthias, Kirschstein, Tobias

原文摘要

We present PercHead, a model for single-image 3D head reconstruction and disentangled 3D editing - two tasks that are inherently challenging due to ambiguity in plausible explanations for the same input. At the heart of our approach lies our novel perceptual loss based on DINOv2 and SAM 2.1. Unlike widely-adopted low-level losses like LPIPS, SSIM or L1, we rely on deep visual understanding of images and the resulting generalized supervision signals. We show that our new loss can be a drop-in replacement for standard losses and used to improve visual quality in high-frequency areas. We base our model architecture on Vision Transformers (ViTs), allowing us to decouple the 3D representation from the 2D input. We train our method on multi-view images for view-consistency and in-the-wild images for strong transferability to new environments. Our model achieves state-of-the-art performance in novel-view synthesis and, furthermore, exhibits exceptional robustness to extreme viewing angles. We also extend our base model to disentangled 3D editing by swapping the encoder and fine-tuning the network. A segmentation map controls geometry and either a text prompt or a reference image specifies appearance. We highlight the intuitive and powerful 3D editing capabilities through an interactive GUI.

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:2c011ab63d5edc1408e7937b015373a2CVF · CVPR 2026

3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction

Noda, Takeshi, Liu, Yu-Shen, Han, Zhizhong

原文摘要

Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose a self-constrained prior to constraining the movement of 3D Gaussians, aiming for more accurate depth rendering. Our self-constrained prior is a TSDF grid fused by the rendered depth during the learning of 3D Gaussians. The prior measures a band on both sides of the estimated surface for imposing more specific constraints on the right 3D Gaussians, such as removing 3D Gaussians outside the band, encouraging larger opacity for Gaussians near the center of the band or smaller opacity for Gaussians near the boundary of the band. We regularly update the prior by fusing more recent depth images which are usually more accurate, and progressively narrow the band to tighten the constraint on Gaussian movements. We justify our idea and report our superiority over the state-of-the-art methods in evaluations on widely used benchmarks.

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:2c02d97096c5d9144f6877bfbcf8ed73CVF · CVPR 2026

VOSR: A Vision-Only Generative Model for Image Super-Resolution

Wu, Rongyuan, Sun, Lingchen, Zhang, Zhengqiang, Kong, Xiangtao, Zhao, Jixin, Wang, Shihao, Zhang, Lei

原文摘要

Large-scale pre-trained text-to-image (T2I) diffusion models, such as Stable Diffusion, can be finetuned for image super-resolution (SR) with highly realistic details. While impressive, pre-training such multi-modal models demands billions of high-quality text-image pairs and substantial computational resources, despite that SR is fundamentally an image-to-image (I2I) task. This raises a critical question: do we truly need multi-modal priors and billion-scale text-image data to solve a purely vision task? In this paper, we propose **VOSR**, a **V**ision-**O**nly **S**uper-**R**esolution framework that eliminates the need for textual priors and multi-modal pretraining. We identify two key limitations in previous image-based, uni-modal diffusion models: limited visual semantic guidance and unstable unconditional training. To this end, we leverage a pretrained vision encoder to inject semantic cues, and introduce a relaxed unconditional objective that partially uses the low-quality condition to stabilize training. To accelerate inference, we adopt a modified shortcut model for one-step SR with minimal quality degradation. VOSR is trained from scratch with significantly less data and a lower computational cost compared to T2I-based diffusion models. However, VOSR achieves comparable or even better performance than state-of-the-art T2I-tuned SR methods on both synthetic and real-world benchmarks, demonstrating its potential as a scalable and competitive alternative for generative SR. Codes and models will be made publicly available.

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:2c1b0205c54b283f22dd6c5091ec053aCVF · CVPR 2026

Tracking through Severe Occlusion via Event-Derived Transient Cues

Dong, Hao, Liu, Yujin, Liu, Haoyue, Wang, Zhenyu, Peng, Shihan, Shi, Zhiwei, Chang, Yi, Yan, Luxin

原文摘要

Tracking targets with high-speed and nonlinear motion under occlusion remains challenging due to spatial appearance deprivation and temporal trajectory fragmentation caused by missing visual cues. Existing methods typically either dynamically update templates to maintain appearance similarity or employ autoregressive models to predict targets from historical trajectories. However, these methods are ineffective under severe occlusion owing to template contamination and limited frame rates for complex motion. In this work, we observe that occlusion inherently degrades the spatial matching mechanism, highlighting the importance of temporal cues. Meanwhile, event cameras with microsecond-level temporal resolution provide transient dynamic cues that facilitate modeling nonlinear motion. In light of this, we propose EvoTrack, an occlusion-robust tracking framework via event-derived transient evolution, which comprises event-based motion autoregression and target-aware appearance matching. Specifically, for motion autoregression, the fine-grained timestamps of events naturally encode the target's direction and speed, motivating a bidirectional motion consistency that constrains inter-frame displacement prediction under nonlinear motion. For appearance matching, we adopt a Gaussian masking strategy to simulate occlusion degradation, guiding the model to focus on target regions and learn invariant representations. Furthermore, we build a pixel-aligned Frame-Event tracking dataset with higher spatial resolution and explicit occlusion labels. Extensive experiments demonstrate the effectiveness of EvoTrack in challenging occlusion scenes.

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:2c3dfb245bcf51a6b93e04f13fb8f363CVF · CVPR 2026

Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

Fang, Shuangkang, Shen, I-Chao, Zhang, Xuanyang, Wang, Zesheng, Wang, Yufeng, Ding, Wenrui, YU, Gang, Igarashi, Takeo

原文摘要

Recent 3D Gaussian Splatting (3DGS) dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. Moreover, these methods overlook the contribution of high-degree spherical harmonic coefficients (SH) to overfitting. To address these issues, we propose DropAnSH-GS, a novel anchor-based dropout strategy. Rather than dropping Gaussians independently, our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors. This effectively disrupts local redundancies and encourages the model to learn more robust, globally informed representations. Furthermore, we extend the dropout to color attributes by randomly dropping higher-degree SH coefficients to concentrate appearance information in lower-degree SH. This strategy further mitigates overfitting and enables flexible post-training model compression via SH truncation. Experimental results demonstrate that DropAnSH-GS substantially outperforms existing dropout methods with negligible computational overhead and can be readily integrated into various 3DGS variants to enhance their performances.

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:2c5a3ae45f363a2a9cd0c5e0255e7a6aCVF · CVPR 2026

Ultra-Fast Neural Video Compression

Li, Jiahao, Xie, Wenxuan, Jia, Zhaoyang, Li, Bin, Guo, Zongyu, Zhang, Xiaoyi, Lu, Yan

原文摘要

While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This paper introduces a chunk-based coding framework designed to significantly improve the rate-distortion-complexity trade-off. Instead of processing frames sequentially, our approach encodes a chunk of multiple frames into a single compact latent representation and decodes them simultaneously. This is enabled by cross-frame interaction modules for joint spatial-temporal modeling and frame-specific decoders for parallel reconstruction. This paradigm not only dramatically enhances coding throughput but also facilitates more effective modeling of long-term temporal correlations. To further boost speed, we propose a streamlined entropy coding mechanism that consolidates bit-stream interactions into a single step, substantially reducing decoding overhead. Building on these innovations, we present DCVC-UF (Ultra-Fast), a new NVC that sets a new SOTA in performance. Our experiments show that DCVC-UF can achieve ultra-fast encoding and decoding speeds, significantly outperforming previous leading codecs. DCVC-UF serves as a notable landmark in the journey of NVC evolution. The code is at https://github.com/microsoft/DCVC.

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:2c75bcf2f35afb552eaab15d91f5ecaeCVF · CVPR 2026

SCE-Depth: A Spherical Compound Eye Framework for Wide FOV Depth Estimation

Zhu, Yi, Xiong, Hao, Xiao, Lin, Shi, Ranfeng, Gu, Qinying, Gu, Leilei

原文摘要

Accurate depth estimation in wide field is highly desired in applications of autonomous driving, robot vision and drone controls. Biological compound eyes inspire wide Field of View (FOV) depth estimation, yet their artificial implementations face the challenge of modality misalignment. Specifically, the spherical imaging data do not align well with planar neural networks, diminishing the learning efficiency. Herein, we propose SCE-Depth, a bio-inspired framework for spherical compound eye depth estimation, which processes spherical images natively on a HEALPix grid using a spherical neural network. This approach achieves a unified 180deg FOV while avoiding the errors typically introduced by modality conversion. Additionally, we identify a depth-sensitive gradient feature from the overlapping FOVs of adjacent ommatidia. To exploit it, we introduce a spherical Sobel operator called the Spherical Gradient Feature Extractor (SGFE) and a corresponding Spherical Gradient Loss (SGL), which jointly extract gradient features on the HEALPix grid, enabling gradient-aware depth prediction. Extensive benchmark experiments demonstrate that these strategies enable SCE-Depth to substantially reduce depth estimation error compared to fisheye-based baselines, with particularly large improvements in peripheral accuracy. We also demonstrate the generalization capability of SCE-Depth to other wide FOV data modalities, such as fisheye and panoramic imagery. Code is available at https://github.com/ZhuYi2000/SCE-Depth.

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:2c9ac2428e465e0dce2e87b73d8357c5CVF · CVPR 2026

MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer

Li, Weiyu, Toisoul, Antoine, Monnier, Tom, Shapovalov, Roman, Ranjan, Rakesh, Tan, Ping, Vedaldi, Andrea

原文摘要

We present MeshFlow, a new method for compressing and generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh connectivity, which, however, scales poorly due to the inference cost being quadratic in mesh size. AR methods also require discretizing the vertex coordinates, which introduces quantization errors and can cause vertex collapse. To address these challenges, we introduce a Variational Autoencoder (VAE) that, supervised with a contrastive loss, represents both continuous vertex positions and discrete connectivity in a continuous latent space.This latent space is significantly more compact than prior token-based mesh representations. We then build a 3D generator based on a Rectified-Flow transformer, which generates all mesh vertices and edges in parallel. This model samples meshes 26xfaster than the fastest AR generator while also achieving state-of-the-art accuracy across standard mesh-generation metrics.

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:2ca6db22386df1c04b8e4e3d2eba6c57CVF · CVPR 2026

RAVEN: Erasing Invisible Watermarks via Novel View Synthesis

Shamshad, Fahad, Lukas, Nils, Nandakumar, Karthik

原文摘要

Invisible watermarking has become a critical mechanism for authenticating AI-generated image content, with major platforms deploying watermarking schemes at scale. However, evaluating the vulnerability of these schemes against sophisticated removal attacks remains essential to assess their reliability and guide robust design. In this work, we expose a fundamental vulnerability in invisible watermarks by reformulating watermark removal as a view synthesis problem. Our key insight is that generating a perceptually consistent alternative view of the same semantic content, akin to re-observing a scene from a shifted perspective, naturally removes the embedded watermark while preserving visual fidelity. This reveals a critical gap: watermarks robust to pixel-space and frequency-domain attacks remain vulnerable to semantic-preserving viewpoint transformations. We introduce a zero-shot diffusion-based framework that applies controlled geometric transformations in latent space, augmented with view-guided correspondence attention to maintain structural consistency during reconstruction. Operating on frozen pre-trained models without detector access or watermark knowledge, our method achieves state-of-the-art watermark suppression across 15 watermarking methods--outperforming 14 baseline attacks while maintaining superior perceptual quality across multiple datasets.

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:2cb0beb037d4ac08d883ace62489cd68CVF · CVPR 2026

Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation

Zhang, Pingrui, Su, Yifei, Wu, Pengyuan, An, Dong, Zhang, Li, Wang, Zhigang, Wang, Dong, Zhao, Bin

原文摘要

Vision-and-Language Navigation (VLN) requires the agent to navigate based on natural instructions. This task is challenging due to partial observability, which makes it difficult to align perception with language. Recent methods mitigate this by imagining future scenes, yet they rely on vision-based synthesis, leading to high computational cost and redundant details. To this end, we propose to adaptively imagine key environmental semantics via language form, enabling a more reliable and efficient strategy. Specifically, we introduce Adaptive Text Dreamer (ATD), a dual-branch self-guided imagination policy built upon a large language model (LLM). ATD is designed with a human-like left-right brain architecture, where the left brain focuses on logical integration, and the right brain is responsible for imaginative prediction of future scenes. To achieve this, we fine-tune only the Q-former within both brains to efficiently activate domain-specific knowledge in the LLM, enabling dynamic updates of logical reasoning and imagination during navigation. Furthermore, we propose a cross-interaction mechanism that regularizes the imagined latent-space outputs and integrates them with the navigation expert module via a decoder-free latent interface, thereby enabling ATD to jointly harness the reasoning ability of the LLM and the task-specific knowledge of the navigation model. We conduct extensive experiments across the R2R, REVERIE, and R4R benchmarks, demonstrating that ATD achieves competitive performance with significantly fewer parameters. The code will be available.

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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CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization

Hou, Xinhai, Xu, Shaoyuan, Biyani, Manan, Li, Moyan, Liu, Jia, Hollon, Todd C, Wang, Bryan

原文摘要

Agentic vision-language models are increasingly trained to "think with images" by calling image operations. However, we show that high final-answer accuracy often hides unfaithful visual reasoning: models may invoke tools on irrelevant regions or ignore tool outputs entirely, yet still guess the correct answer. In this work, we first propose a faithfulness evaluation protocol that measures whether intermediate visual tool outputs (e.g., crops) actually contain the queried evidence. This reveals that recent visual agents achieve high final-answer accuracy but exhibit low rates of faithful tool-use on visual search benchmarks. We then introduce CodeV, a code-based visual agent trained with Tool-Aware Policy Optimization (TAPO). TAPO is a process-level RL framework that augments GRPO with dense rewards defined directly on visual tool inputs and outputs, rather than on chain-of-thought tokens, making supervision easier to verify and less susceptible to reward hacking. CodeV represents visual tools as executable Python code, and TAPO assigns step-wise rewards based solely on the question and tool output, encouraging both necessary and evidence-consistent tool use. In a two-stage SFT+RL pipeline, CodeV achieves competitive or superior accuracy while substantially increasing faithful tool-use rates on related visual search benchmarks. Beyond visual search, CodeV attains strong performance on a range of multimodal reasoning and math benchmarks, suggesting that explicitly supervising intermediate tool behavior is crucial for building trustworthy, agentic visual reasoning systems.

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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Reliev3R: Relieving Feed-forward 3D Reconstruction from Multi-View Geometric Annotations

Chen, Youyu, Jiang, Junjun, Luo, Yueru, Jiang, Kui, Liu, Xianming, Yan, Xu, Chen, Dave Zhenyu

原文摘要

With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up.In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models.At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency.Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs.

cs.CV

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

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Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation

Wang, Fei, Zheng, Xinye, Li, Kun, Wei, Yanyan, Liu, Yuxin, Hu, Ganpeng, Bao, Tong, Yang, Jingwen

原文摘要

Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canonical parameters such as the turnover number (k_\text cat ), Michaelis constant (K_\text m ), and inhibition constant (K_\text i ) depend jointly on the enzyme sequence, the substrate chemistry, and the conformational adaptation of the active site during binding. Many learning pipelines simplify this process to a static compatibility problem between the enzyme and substrate, fusing their representations through shallow operations and regressing a single value. Such formulations overlook the staged nature of catalysis, which involves both substrate recognition and conformational adaptation. In this regard, we reformulate kinetic prediction as a staged multimodal conditional modeling problem and introduce the Enzyme-Reaction Bridging Adapter (ERBA), which injects cross-modal information via fine-tuning into Protein Language Models (PLMs) while preserving their biochemical priors. ERBA performs conditioning in two stages: Molecular Recognition Cross-Attention (MRCA) first injects substrate information into the enzyme representation to capture specificity; Geometry-aware Mixture-of-Experts (G-MoE) then integrates active-site structure and routes samples to pocket-specialized experts to reflect induced fit. To maintain semantic fidelity, Enzyme-Substrate Distribution Alignment (ESDA) enforces distributional consistency within the PLM manifold in a reproducing kernel Hilbert space. Experiments across three kinetic endpoints and multiple PLM backbones, ERBA delivers consistent gains and stronger out-of-distribution performance compared with sequence-only and shallow-fusion baselines, offering a biologically grounded route to scalable kinetic prediction and a foundation for adding cofactors, mutations, and time-resolved structural cues.

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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MARCO: Navigating the Unseen Space of Semantic Correspondence

Cuttano, Claudia, Trivigno, Gabriele, Masone, Carlo, Roth, Stefan

原文摘要

Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried points rarely match those seen during training. Building upon DINOv2, we introduce MARCO, a unified model for generalizable correspondence driven by a novel training framework that enhances both fine-grained localization and semantic generalization. By coupling a coarse-to-fine objective that refines spatial precision with a self-distillation framework, which expands sparse supervision beyond annotated regions, our approach transforms a handful of keypoints into dense, semantically coherent correspondences. MARCO sets a new state of the art on SPair-71k, AP-10K, and PF-PASCAL, with gains that amplify at fine-grained localization thresholds (+8.9 PCK@0.01), strongest generalization to unseen keypoints (+5.1, SPair-U) and categories (+4.7, MP-100), while remaining 3x smaller and 10x faster than diffusion-based approaches.

cs.CV

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

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SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models

Liu, Haowen, Yao, Shaoxiong, Chen, Haonan, Gao, Jiawei, Mao, Jiayuan, Huang, Jia-Bin, Du, Yilun

原文摘要

Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities. However, they lack a grounded understanding of physical dynamics. This limitation arises from training VLMs on static internet-scale visual-language data that contain no causal interactions or action-conditioned changes. Consequently, it remains challenging to leverage VLMs for fine-grained robotic manipulation tasks that require physical understanding, reasoning, and corresponding action planning. To overcome this, we present SIMPACT, a test-time, SIMulation-enabled ACTion Planning framework that equips VLMs with physical reasoning through simulation-in-the-loop world modeling, without requiring any additional training. From a single RGB-D observation, SIMPACT efficiently constructs physics simulations, enabling the VLM to propose informed actions, observe simulated rollouts, and iteratively refine its reasoning. By integrating language reasoning with physics prediction, our simulation-enabled VLM can understand contact dynamics and action outcomes in a physically grounded way. Our method demonstrates state-of-the-art performance on five challenging, real-world rigid-body and deformable manipulation tasks that require fine-grained physical reasoning, outperforming existing general-purpose robotic manipulation models. Our results demonstrate that embedding physics understanding via efficient simulation into VLM reasoning at test time offers a promising path towards generalizable embodied intelligence.

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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Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models

Zaleska, Katarzyna, Popek, Łukasz, Wysoczańska, Monika, Deja, Kamil

原文摘要

Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture. While existing localization techniques focus on prompt-related interventions, we notice that such explicit conditioning may differ from implicit decisions. Therefore, we introduce a probing-based localization technique to identify the layers with the highest attribute separability for concepts. Our findings indicate that the resolution of ambiguous concepts is governed principally by self-attention layers, identifying them as the most effective point for intervention. Based on this discovery, we propose ICM (Implicit Choice-Modification) - a precise steering method that applies targeted interventions to a small subset of layers. Extensive experiments confirm that intervening on these specific self-attention layers yields superior debiasing performance compared to existing state-of-the-art methods, minimizing artifacts common to less precise approaches.

cs.CV

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

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DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution

Yesiltepe, Hidir, PNVR, Koutilya, Pathak, Gaurav, Bodla, Navaneeth, Singh, Bharat, Yanardag, Pinar, Xie, Jinrong

原文摘要

Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the strong coupling between conditional and unconditional branches in standard classifier-free guidance. We introduce a training-free framework that enhances distorted and low-resolution videos by decoupling these signals in time. Our proposed Decoupled Time Guidance (DTG) evaluates the unconditional branch at a cleaner diffusion timestep, providing a lookahead prior that preserves geometry while suppressing replication of warped content. This temporal bias is annealed throughout sampling, allowing the model to transition from structure correction to detail refinement without retraining. Combined with any off-the-shelf restoration module in a plug-and-play manner, our approach improves perceptual coherence and restores plausible structure in AI-generated and real-world videos alike. To facilitate evaluation, we curate GenWarp480, a benchmark of 4,400 distorted 480p videos synthesized from diverse text-to-video models. GenWarp480 focuses on characteristic generative degradations such as warped faces, body misalignments, and spatial artifacts, providing a purpose-built testbed for assessing robustness to generative errors. Extensive experiments demonstrate that our method achieves significant improvements in structural fidelity and temporal stability without any model training.

cs.CV

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

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VAD-GS: Visibility-Aware Densification for 3D Gaussian Splatting in Dynamic Urban Scenes

Zhang, Yikang, Fan, Rui

原文摘要

3D Gaussian splatting (3DGS) has demonstrated impressive performance in synthesizing high-fidelity novel views. Nonetheless, its effectiveness critically depends on the quality of the initialized point cloud. Specifically, achieving uniform and complete point coverage over the underlying scene structure requires overlapping observation frustums, an assumption that is often violated in unbounded, dynamic urban environments. Training Gaussian models with partially initialized point clouds often leads to distortions and artifacts, as camera rays may fail to intersect valid surfaces, resulting in incorrect gradient propagation to Gaussian primitives associated with occluded or invisible geometry. Additionally, existing densification strategies simply clone and split Gaussian primitives from existing ones, incapable of reconstructing geometry from missing structures. To address these limitations, we propose VAD-GS, a 3DGS framework tailored for geometry recovery in challenging urban scenes. Our method identifies unreliable geometry structures via voxel-based visibility reasoning, selects informative supporting views through diversity-aware view selection, and recovers missing structures via multi-view stereo reconstruction. This design enables the generation of new Gaussian primitives guided by reliable geometric priors, even in regions lacking initial points. Extensive experiments on the Waymo and nuScenes datasets demonstrate that VAD-GS outperforms state-of-the-art 3DGS approaches and significantly improves the quality of reconstructed geometry for both static and dynamic objects.Source code will be released upon publication.

cs.CV

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

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CoVFT: Context-aware Visual Fine-tuning for Multimodal Large Language Models

Zhou, Nan, Wang, Huiqun, Zheng, Yaoyan, Huang, Di

原文摘要

Multimodal large language models (MLLMs) achieve remarkable progress in cross-modal perception and reasoning, yet a fundamental question remains unresolved: should the vision encoder be fine-tuned or frozen? Despite the success of models such as LLaVA and Qwen-VL, inconsistent design choices and heterogeneous training setups hinder a unified understanding of visual fine-tuning (VFT) in MLLMs. Through a configuration-aligned benchmark, we find that existing VFT methods fail to consistently outperform the frozen baseline across multimodal tasks. Our analysis suggests that this instability arises from visual preference conflicts, where the context-agnostic nature of vision encoders induces divergent parameter updates under diverse multimodal context. To address this issue, we propose the Context-aware Visual Fine-tuning (CoVFT) framework, which explicitly incorporates multimodal context into visual adaptation. By integrating a Context Vector Extraction (CVE) and a Contextual Mixture-of-Experts (CoMoE) module, CoVFT decomposes conflicting optimization signals and enables stable, context-sensitive visual updates. Extensive experiments on 12 multimodal benchmarks demonstrate that CoVFT achieves state-of-the-art performance with superior stability. Notably, fine-tuning a 7B MLLM with CoVFT surpasses the average performance of its 13B counterpart, revealing substantial untapped potential in visual encoder optimization within MLLMs. Code is available at https://github.com/weeknan/CoVFT.

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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EasyV2V: A High-quality Instruction-based Video Editing Framework

Mai, Jinjie, Wang, Chaoyang, Qian, Gordon Guocheng, Menapace, Willi, Tulyakov, Sergey, Ghanem, Bernard, Wonka, Peter, Mirzaei, Ashkan

原文摘要

While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization.We study the design space of data, architecture, and control, and introduce EasyV2V, a simple and effective framework for instruction-based video editing. On the data side, we compose existing experts with fast inverses to build diverse video pairs, lift image edit pairs into videos via single-frame supervision and pseudo pairs with shared affine motion, mine dense-captioned clips for video pairs, and add transition supervision to teach how edits unfold.On the model side, we observe that pretrained text-to-video models possess editing capability, motivating a simplified design. Simple sequence concatenation for conditioning with light LoRA fine-tuning suffices to train a strong model.For control, we unify spatiotemporal control via a single mask mechanism and support optional reference images.Overall, EasyV2V works with flexible inputs, e.g., video+text, video+mask+text, video+mask+reference+text, and achieves state-of-the-art video editing results, surpassing concurrent and commercial systems.

cs.CV

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

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Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis

Gao, Jianzhe, Wang, Churan, Zhang, Weiyi, Li, Jianghua, Li, Li-An, Wang, Wenguan, Zhu, Yixin, Wang, Yizhou

原文摘要

Clinical video diagnosis, in which physicians assess dynamic tissue responses across procedural stages, is critical for detecting diseases such as cervical and colorectal cancers. Recent spatiotemporal models map visual progressions directly to diagnostic outputs, yet overlook two hallmarks of expert reasoning: clinically grounded diagnostic principles and hypothesis-driven counterfactual thinking. This gap leads existing methods to conflate causal pathological cues with non-pathological variations, thereby limiting their reliability in data-scarce settings. Here we show that explicitly modeling counterfactual tissue evolution, guided by clinical rules that encode expert diagnostic principles, substantially improves diagnostic robustness-emulating the hypothesis-driven reasoning clinicians employ in practice. We introduce MEDVCR, comprising a Counterfactual Generator (CG) that synthesizes hypothetical tissue transitions via diffusion modeling, a Counterfactual Representation Learning (CRL) module that enforces temporal consistency, pathological separability, and counterfactual alignment, and a Dual Diagnostic Prediction (DDP) strategy that combines video-level context with frame-level counterfactual contrast. On two representative tasks, MEDVCR achieves 93.0% Recall@1 on colposcopy (+10.2%) and 94.8% Average Precision (AP) on colonoscopy (+2.6%), outperforming all state-of-the-art (SOTA) baselines. We anticipate that this counterfactual reasoning paradigm will open new avenues for vision-based clinical diagnostic tasks toward more transparent and interpretable decision support.

cs.CV

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

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REVIVE 3D: Refinement via Encoded Voluminous Inflated prior for Volume Enhancement

Lee, Hankyeol, Baek, Wooyeol, Kim, Seongdo, Kim, Jongyoo

原文摘要

Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision and graphics. However, these models still struggle to generate voluminous 3D assets when the input is a flat image that provides limited 3D cues. We introduce REVIVE 3D, a two-stage, plugand-play pipeline for generating voluminous 3D assets from flat images. In Stage 1, we construct an Inflated Prior by inflating the foreground silhouette to recover global volume and superimposing part-aware details to capture local structure. In Stage 2, 3D Latent Refinement injects Gaussian noise into the Inflated Prior's latent and then denoises it, using the prior's geometric cues to leverage the backbone's pretrained 3D knowledge. Furthermore, our framework supports image-conditioned 3D editing. To quantify volume and surface flatness, we propose Compactness and Normal Anisotropy. We validate Compactness and Normal Anisotropy through a user study, showing that these metrics align with human perception of volume and quality. We show that REVIVE 3D achieves state-of-the-art performance on a challenging flat image dataset, based on extensive qualitative and quantitative evaluations.

cs.CV

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

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UniPercept: A Unified Diffusion Model for Generalizable Visual Perception

Zhao, Zuyan, He, Zhenliang, Kan, Meina, Shan, Shiguang, Chen, Xilin

原文摘要

Diffusion models have shown impressive performance in generative tasks, demonstrating their ability to capture detailed structural and semantic information. Recently, these capabilities have been extended to visual understanding, with studies employing diffusion models as the backbone for various perception tasks. However, existing diffusion-based perception models are generally restricted to a single task or a fixed set of predefined tasks, lacking an efficient mechanism to generalize to novel tasks. To overcome this limitation, we propose a unified DiT-based perception framework called UniPercept, which introduces a novel foundation-adapter paradigm for general visual perception. In this framework, a shared diffusion-based foundation model is trained to capture common and generalizable visual knowledge across diverse perception tasks, with task-specific adapters integrated for each individual task. Leveraging its superior generalization ability, the foundation model can be efficiently adapted to novel domains through lightweight adapters, requiring as few as **1,000** training samples and less than **1%** of trainable parameters. Furthermore, UniPercept demonstrates strong performance across various perception tasks, outperforming state-of-the-art generalist models in most cases and achieving accuracy comparable to specialist models. Project page: https://VIPL-GENUN.github.io/Project-UniPercept.

cs.CV

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

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