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首次观测 · 记录 #1CVF · CVPR 2026:cvf:43b04b92ffd0180cc05126d87191241bCVF · CVPR 2026

CodePercept: Code-Grounded Visual STEM Perception for MLLMs

Guan, Tongkun, Yang, Zhibo, Wan, Jianqiang, Yang, Mingkun, Guo, Zhentao, Hu, Zijian, Luo, Ruilin, Chen, Ruizhe 等 13 位作者

原文摘要

When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual deficiencies or reasoning limitations? Through systematic scaling analysis that independently scales perception and reasoning components, we uncover a critical insight: scaling perception consistently outperforms scaling reasoning. This reveals perception as the true lever limiting current STEM visual reasoning. Motivated by this insight, our work focuses on systematically enhancing the perception capabilities of MLLMs by establishing code as a powerful perceptual medium--executable code provides precise semantics that naturally align with the structured nature of STEM visuals. Specifically, we construct ICC-1M, a large-scale dataset comprising 1M Image-Caption-Code triplets that materializes this code-as-perception paradigm through two complementary approaches: (1) Code-Grounded Caption Generation treats executable code as ground truth for image captions, eliminating the hallucinations inherent in existing knowledge distillation methods; (2) STEM Image-to-Code Translation prompts models to generate reconstruction code, mitigating the ambiguity of natural language for perception enhancement. To validate this paradigm, we further introduce STEM2Code-Eval, a novel benchmark that directly evaluates visual perception in STEM domains. Unlike existing work relying on problem-solving accuracy as a proxy that only measures problem-relevant understanding, our benchmark requires comprehensive visual comprehension through executable code generation for image reconstruction, providing deterministic and verifiable assessment. Code is available at https://github.com/TongkunGuan/Qwen-CodePercept.

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:4d2303f4a439e046ec7153f89c4e7634CVF · CVPR 2026

RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark

Shi, Yang, Dong, Yuhao, Ding, Yue, Wang, Yuran, Zhu, Xuanyu, Zhou, Sheng, Liu, Wenting, Tian, Haochen 等 25 位作者

原文摘要

The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question remains unanswered by existing benchmarks: does this architectural unification actually enable synergetic interaction between the constituent capabilities? Existing evaluation paradigms, which primarily assess understanding and generation in isolation, are insufficient for determining whether a unified model can leverage its understanding to enhance its generation, or use generative simulation to facilitate deeper comprehension. To address this critical gap, we introduce RealUnify, a benchmark specifically designed to evaluate bidirectional capability synergy. RealUnify comprises 1,000 meticulously human-annotated instances spanning 10 categories and 32 subtasks. It is structured around two core axes: 1) Understanding Enhances Generation, which requires reasoning (e.g., commonsense, logic) to guide image generation, and 2) Generation Enhances Understanding, which necessitates mental simulation or reconstruction (e.g., of transformed or disordered visual inputs) to solve reasoning tasks. A key contribution is our dual-evaluation protocol, which combines direct end-to-end assessment with a diagnostic stepwise evaluation that decomposes tasks into distinct understanding and generation phases. This protocol allows us to precisely discern whether performance bottlenecks stem from deficiencies in core abilities or from a failure to integrate them. Through large-scale evaluations of 12 leading unified models and 6 specialized baselines, we find that current unified models still struggle to achieve effective synergy, indicating that architectural unification alone is insufficient. These results highlight the need for new training strategies and inductive biases to fully unlock the potential of unified modeling.

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:4d492f8b21de479f14553d9d6154f90eCVF · CVPR 2026

CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation

Su, Xia, Chen, Ruiqi, Liu, Benlin, Ma, Jingwei, Di, Zonglin, Krishna, Ranjay, Froehlich, Jon

原文摘要

Vision-Language Models (VLMs) have shown remarkable progress in Vision-Language Navigation (VLN), offering new possibilities for navigation decision-making that could benefit both robotic platforms and human users. However, real-world navigation is inherently conditioned by the agent's mobility constraints. For example, a sweeping robot cannot traverse stairs, while a quadruped can. We introduce Capability-Conditioned Navigation (CapNav), a benchmark designed to evaluate how well VLMs can navigate complex indoor spaces given an agent's specific physical and operational capabilities. CapNav defines five representative human and robot agents, each described with physical dimensions, mobility capabilities, and environmental interaction abilities. CapNav provides 45 real-world indoor scenes, 473 navigation tasks, and 2365 QA pairs to test if VLMs can traverse indoor environments based on agent capabilities. We evaluate 13 modern VLMs and find that current VLM's navigation performance drops sharply as mobility constraints tighten, and that even state-of-the-art models struggle with obstacle types that require reasoning on spatial dimensions. We conclude by discussing the implications for capability-aware navigation and the opportunities for advancing embodied spatial reasoning in future VLMs. The benchmark is available at https://makeabilitylab.github.io/CapNav/

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:58db05fc6eb4b557f776937bfe61ac72CVF · CVPR 2026

Layer-wise Instance Binding for Regional and Occlusion Control in Text-to-Image Diffusion Transformers

Chen, Ruidong, Bai, Yancheng, Zhang, Xuanpu, Zeng, Jianhao, Wang, Lanjun, Song, Dan, Sun, Lei, Chu, Xiangxiang 等 9 位作者

原文摘要

Region-instructed layout control in text-to-image generation is highly practical, yet existing methods suffer from limitations: (i) training-based approaches inherit data bias and often degrade image quality, and (ii) current techniques struggle with occlusion order, limiting real-world usability. To address these issues, we propose LayerBind. By modeling regional generation as distinct layers and binding them during the generation, our method enables precise regional and occlusion controllability. Our motivation stems from the observation that spatial layout and occlusion are established at a very early denoising stage, suggesting that rearranging the early latent structure is sufficient to modify the final output. Building on this, we structure the scheme into two phases: instance initialization and subsequent semantic nursing. (1) First, leveraging the contextual sharing mechanism in multimodal joint attention, Layer-wise Instance Initialization creates per-instance branches that attend to their own regions while anchoring to the shared background. At a designated early step, these branches are fused according to the layer order to form a unified latent with a pre-established layout. (2) Then, Layer-wise Semantic Nursing reinforces regional details and maintains the occlusion order via a layer-wise attention enhancement. Specifically, a sequential layered attention path operates alongside the standard global path, with updates composited under a layer-transparency scheduler. LayerBind is training-free and plug-and-play, serving as a regional and occlusion controller across Diffusion Transformers. It also supports editable workflows, allowing for flexible modifications like changing instances or rearranging visible orders. Experimental results demonstrate LayerBind's effectiveness, highlighting its potential for creative applications. Project page: https://littlefatshiba.github.io/layerbind-page

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:7b69d0e5b6525f5d620be00fe5de3177CVF · CVPR 2026

Spe-BEVHead: Rethinking the Detection Head Design for Bird's-Eye-View Object Detection

Zhang, Junshu, Zhao, Sicheng, Zhao, Xin, Yang, Fan, Chen, Ruike, Han, Jungong, Ding, Guiguang

原文摘要

Bird's-Eye-View (BEV) detection has become a dominant paradigm for 3D object detection in autonomous driving, due to its strong perception capability. However, most existing methods mainly focus on constructing high-quality BEV feature representations, while neglecting the design of task-specific detection heads. In practice, they directly adopt the center-based head originally developed for 2D detection, without any specific optimization. This leads to three inherent limitations: (i) a geometric mismatch between the Gaussian kernel used for classification and the real BEV object, (ii) degraded end-to-end performance without Non-Maximum Suppression (NMS), and (iii) sparse supervisory signals. To address these issues, we propose Spe-BEVHead, a detection head specifically tailored for BEV 3D object detection. Spe-BEVHead introduces three BEV-specific adaptations: (1) a Rotated Box Kernel that generates geometry-aligned classification weights, (2) a Local Response Refinement Module (LRRM) that suppresses non-peak responses and improves end-to-end performance, and (3) a dual-branch architecture that provides richer supervisory signals to promote more robust learning while inherently preserving the performance for end-to-end inference. Extensive experiments show that Spe-BEVHead can be seamlessly integrated into existing BEV backbones, delivering direct performance gains while retaining competitive performance under the challenging end-to-end setting.

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:83c1c4275485e1bb543ecabc641f7ce3CVF · CVPR 2026

From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

Qu, Xiangyan, Yuan, Zhenlong, Tang, Jing, Chen, Rui, Tang, Datao, Yu, Meng, Sun, Lei, Bai, Yancheng 等 12 位作者

原文摘要

Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image and instruction. This mismatch causes three challenges when applying Image-CoT to editing: inefficient resource allocation with fixed sampling budgets, unreliable early-stage verification using general MLLM scores, and redundant edited results from large-scale sampling. To address this, we propose ADaptive Edit-CoT (ADE-CoT), an on-demand test-time scaling framework to enhance editing efficiency and performance. It incorporates three key strategies: (1) a difficulty-aware resource allocation that assigns dynamic budgets based on estimated edit difficulty; (2) edit-specific verification in early pruning that uses region localization and caption consistency to select promising candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.

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:8dc707007d5877169df413d9ddef7d59CVF · CVPR 2026

TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

Luo, Guan, Li, Xiu, Chen, Rui, Yi, Xuanyu, Lin, Jing, Chen, Chia Hao, Liu, Jiahang, Zhang, Song-Hai 等 9 位作者

原文摘要

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (e.g., SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an Linfinity distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details, as shown in Fig. 1.

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:9d1e42af09feee1df011070c71c03544CVF · CVPR 2026

Video-CoE: Reinforcing Video Event Prediction via Chain of Events

Su, Qile, Tang, Jing, Chen, Rui, Sun, Lei, Chu, Xiangxiang

原文摘要

Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose **C**hain **o**f **E**vents (**CoE**) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task.

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:cdb29255f744b3d208121b1466cf681fCVF · CVPR 2026

Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

Jiang, Songtao, Song, Sibo, Zhou, Chenyi, Wang, Yuan, Chen, Ruizhe, Guan, Tongkun, Luo, Ruilin, Zhang, Yan 等 15 位作者

原文摘要

The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion trajectories, speed changes, and state transitions. Yet current post-training methods fall short due to two critical limitations: (1) existing datasets often lack temporal-centricity, where answers can be inferred from isolated keyframes rather than requiring holistic temporal integration; (2) training data generated by proprietary models contains systematic errors in fundamental temporal perception, such as confusing motion directions or misjudging speeds. We introduce SynRL, a post-training framework that teaches models temporal primitives, the fundamental building blocks of temporal understanding including direction, speed, and state tracking. Our key insight is that these abstract primitives, learned from programmatically generated synthetic videos, transfer effectively to real-world scenarios. We decompose temporal understanding into short-term perceptual primitives (speed, direction) and long-term cognitive primitives (state tracking, retrodictive inference), constructing 7.7K CoT and 7K RL samples with ground-truth frame-level annotations through code-based video generation. Despite training on simple geometric shapes, SynRL achieves substantial improvements across 15 benchmarks spanning temporal grounding, complex reasoning, and general video understanding. Remarkably, our 7.7K synthetic CoT samples outperform Video-R1's 165K real-world samples. We attribute this to fundamental temporal skills--such as tracking frame-by-frame changes and comparing velocity--that transfer effectively from abstract synthetic patterns to complex real-world scenarios. This establishes a new paradigm for video post-training: video temporal learning through carefully designed synthetic data provides a more cost-efficient scaling path. Code is released on https://github.com/jiangsongtao/Synthetic-Video.

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:dbde0b34f454766d84b41cf4acca1e92CVF · CVPR 2026

RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment

Jiang, Liyao, Chen, Ruichen, Gao, Chao, Niu, Di

原文摘要

Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine-grained attributes. Existing training-free inference-time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection-tuned models require carefully curated reflection datasets and extensive joint fine-tuning of diffusion and vision-language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement-Adaptive Self-Improving Evolution), a training-free, requirement-driven evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement-driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions--including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test-time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state-of-the-art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30-40%) and VLM calls (reduced by 80%) than prior scaling and reflection-tuned baselines, demonstrating efficient, generalizable, and model-agnostic multi-round self-improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.

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:ed8af4d21308258d66b55eb5192ff54dCVF · CVPR 2026

Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following

Xiong, Tianyi, Ge, Yi, Li, Ming, Zhang, Zuolong, Kulkarni, Pranav, Wang, Kaishen, He, Qi, Zhu, Zeying 等 16 位作者

原文摘要

Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences. However, their ability to follow diverse, fine-grained evaluation criteria remains underexplored. We develop Multi-Crit, a benchmark for evaluating multimodal judges on their capacity to follow pluralistic criteria and produce reliable criterion-level judgments. Covering both open-ended generation and verifiable reasoning tasks, Multi-Crit is built through a rigorous data curation pipeline that gathers challenging response pairs with multi-criterion human annotations. It further introduces three novel metrics for systematically assessing pluralistic adherence, criterion-switching flexibility, and the ability to recognize criterion-level preference conflicts. Comprehensive analysis of 25 LMMs reveals that 1) proprietary models still struggle to maintain consistent adherence to pluralistic criteria--especially in open-ended evaluation; 2) open-source models lag further behind in flexibly following diverse criteria; and 3) critic fine-tuning with holistic judgment signals enhances visual grounding but fails to generalize to pluralistic criterion-level judgment. Additional analyses on reasoning fine-tuning, test-time scaling, and boundary consistency between open-source and proprietary models further probe the limits of current multimodal judges. As a pioneering study, Multi-Crit lays the foundation for building reliable and steerable multimodal AI evaluation.

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:efdef1a06966f3fc70ba8690430d4e00CVF · CVPR 2026

UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

Liang, Yixun, Luo, Kunming, Chen, Xiao, Chen, Rui, Yan, Hongyu, Li, Weiyu, Liu, Jiarui, Tian, Fei-Peng 等 9 位作者

原文摘要

We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based models in the second stage to refine textures after reprojecting the generated multi-view images onto the 3D shapes, which introduces challenges related to topological ambiguity. To address this, we bypass the limitations of UV mapping by introducing a Large Texturing Model (LTM) that directly regresses textures in a unified 3D functional space. Moreover, to enable more effective and complete supervision of LTM, we propose to extend surface-defined textures into a continuous volumetric field to serve as an advanced training objective, which we refer to as Texture Functions (TF). Finally, we develop an advanced LoRA-based strategy for efficiently adapting large-scale 2D Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as our first stage. Extensive experiments demonstrate that UniTEX achieves superior visual quality and texture integrity compared to existing approaches, offering a generalizable and scalable solution for automated 3D texture generation. Code is available in: https://github.com/HKUST-SAIL/UniTEX

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:f70a86b40c9db27d45b0f8e6bdc5462cCVF · CVPR 2026

ViLearn: Accelerating Training Convergence of Image-to-3D Generation via Visibility Learning

Chen, Rui, Zhang, Jianfeng, Lin, Jing, Yi, Xuanyu, Liang, Yixun, Luo, Guan, Li, Xiu, Li, Zeming 等 9 位作者

原文摘要

Single-image-to-3D shape generation has seen remarkable progress, driven by latent diffusion models trained on the compressed latent space of 3D VAEs. However, the task remains intrinsically ill-posed: recovering complete 3D geometry--especially occluded surfaces--from a single view is inherently ambiguous. Existing VecSet-based approaches further exacerbate this challenge by treating shape tokens as an unordered set without explicit positional encoding. This design forces diffusion models to simultaneously learn visible correspondences from the input image and hallucinate invisible geometry within a large, permutation-invariant token space, where the lack of structure significantly hinders training efficiency and convergence stability.To address this, we propose Visibility Learning, a training paradigm that injects visibility structure and positional inductive bias into the image-to-3D pipeline. Our method comprises two synergistic components: (1) Visibility Grouping (VG), which explicitly partitions VecSet tokens into visible and invisible subsets by exploiting the spatial locality of VecSet VAE decoders; and (2) Visibility-Aware Positional Encoding (VAPE), which assigns shared positional embeddings to image tokens and visible shape tokens to amplify their correspondence, while using distinct encodings for invisible tokens to guide hallucination. By explicitly disentangling visible reconstruction from invisible hallucination, our approach shrinks the effective hypothesis space and provides clear structural guidance for diffusion models. Extensive experiments demonstrate that Visibility Learning accelerates training convergence by up to 4.4xwhile achieving superior generation quality compared to strong VecSet-based baselines.

cs.CV

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

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