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首次观测 · 记录 #1CVF · CVPR 2026:cvf:23872da820c47a98423cad5bc5367a5fCVF · CVPR 2026

GeodesicNVS: Probability Density Geodesic Flow Matching for Novel View Synthesis

Wang, Xuqin, Wu, Tao, Zhang, Yanfeng, Liu, Lu, Sun, Mingwei, Wang, Yongliang, Zeller, Niclas, Cremers, Daniel

原文摘要

Recent advances in generative modeling have substantially enhanced novel view synthesis, yet maintaining consistency across viewpoints remains challenging. Diffusion-based models rely on stochastic noise-to-data transitions, which obscure deterministic structures and yield inconsistent view predictions. We advocate a Data-to-Data Flow Matching framework that learns deterministic transformations between paired views, enhancing view-consistent synthesis through explicit data coupling. Building on this, we propose Probability Density Geodesic Flow Matching (PDG-FM), which aligns interpolation trajectories with density-based geodesics of a data manifold. To enable tractable geodesic estimation, we employ a teacher-student framework that distills density-based geodesic interpolants into an efficient ambient-space predictor. Empirically, our method surpasses diffusion-based baselines on Objaverse and GSO30 datasets, demonstrating improved structural coherence and smoother transitions across views. These results highlight the advantages of incorporating data-dependent geometric regularization into deterministic flow matching for consistent novel view generation.

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:30b1c7cf5af98b8490599568bef4e980CVF · CVPR 2026

GenColorBench: A Color Evaluation Benchmark for Text-to-Image Generation

Butt, Muhammad Atif, Gomez-Villa, Alexandra, Wu, Tao, Vazquez-Corral, Javier, Van De Weijer, Joost, Wang, Kai

原文摘要

Recent years have seen impressive advances in text-to-image generation, with image generative or unified models, generating high-quality images from text. Yet these models still struggle with fine-grained color control, often failing to accurately match colors specified in text prompts. While existing benchmarks evaluate compositional reasoning and prompt adherence, none systematically assess the color precision. Color is fundamental to human visual perception and communication, and critical for applications from art to design workflows requiring brand consistency. However, current benchmarks either neglect color or rely on coarse assessments, missing key capabilities like interpreting RGB values or aligning with human expectations. To this end, we propose GenColorBench, the first comprehensive benchmark for T2I color generation, grounded in color systems like ISCC-NBS and CSS3/X11, including numerical colors which are absent elsewhere. With 44K color-focused prompts covering 400+ colors, it reveals models' true capabilities via perceptual and automated assessments. Evaluations of popular T2I models on GenColorBench reveal significant performance variation, indicating which color conventions models understand the best and exposing their failure modes. Furthermore, GenColorBench provides insights to guide future improvements in precise color generation. The benchmark is available at https://moatifbutt.github.io/gencolorbench/.

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:62c96e38bd7bde59980dda6ab3e1eeabCVF · CVPR 2026

MultiCrafter: High-Fidelity Multi-Subject Generation via Disentangled Attention and Identity-Aware Preference Alignment

Wu, Tao, Jiang, Yibo, Lu, Yehao, Wang, Zhizhong, Huang, Zeyi, Qin, Zequn, Li, Xi

原文摘要

Multi-subject image generation aims to synthesize user-provided subjects in a single image while preserving subject fidelity, ensuring prompt consistency, and aligning with human aesthetic preferences. Existing In-Context-Learning based methods are limited by their highly coupled training paradigm. These methods attempt to achieve both high subject fidelity and multi-dimensional human preference alignment within a single training stage, relying on a single, indirect reconstruction loss, which is difficult to simultaneously satisfy both these goals. To address this, we propose MultiCrafter, a framework that decouples this task into two distinct training stages. First, in a pre-training stage, we introduce an explicit positional supervision mechanism that effectively resolves attention bleeding and drastically enhances subject fidelity. Second, in a post-training stage, we propose Identity-Preserving Preference Optimization, a novel online reinforcement learning framework. We feature a scoring mechanism to accurately assess multi-subject fidelity based on the Hungarian matching algorithm, which allows the model to optimize for aesthetics and prompt alignment while ensuring subject fidelity achieved in the first stage. Experiments validate that our decoupling framework significantly improves subject fidelity while aligning with human preferences better.

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:7315eb44e85f1d0306cb51116e4b887bCVF · CVPR 2026

SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization

Chen, Yang, Chen, Xieyuanli, Li, Junxiang, Tang, Jie, Wu, Tao

原文摘要

Robust cross-view geo-localization (CVGL) remains challenging despite the surge in recent progress. Existing methods still rely on field-of-view (FoV)-specific training paradigms, where models are optimized under a fixed FoV but collapse when tested on unseen FoVs and unknown orientations. This limitation necessitates deploying multiple models to cover diverse variations. Although studies have explored dynamic FoV training by simply randomizing FoVs, they failed to achieve robustness across diverse conditions---implicitly assuming all FoVs are equally difficult. To address this gap, we present SinGeo, a simple yet powerful framework that enables a single model to realize robust cross-view geo-localization without additional modules or explicit transformations. SinGeo employs a dual discriminative learning architecture that enhances intra-view discriminability within both ground and satellite branches, and is the first to introduce a curriculum learning strategy to achieve robust CVGL. Extensive evaluations on four benchmark datasets reveal that SinGeo sets state-of-the-art (SOTA) results under diverse conditions, and notably outperforms methods specifically trained for extreme FoVs. Beyond superior performance, SinGeo also exhibits cross-architecture transferability. Furthermore, we propose a consistency evaluation method to objectively assess model stability under varying views, providing an objective perspective for understanding and advancing robustness in future CVGL research. Codes are available at: https://github.com/Yangchen-nudt/SinGeo.

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:c286294e3c7ea0c01a13a24c616d82f9CVF · CVPR 2026

When Robots Should Say ''I Don't Know'': Benchmarking Abstention in Embodied Question Answering

Wu, Tao, Zhou, Chuhao, Zhao, Guangyu, Cao, Haozhi, Pu, Yewen, Yang, Jianfei

原文摘要

Embodied Question Answering (EQA) requires an agent to interpret language, perceive its environment, and navigate within 3D scenes to produce responses. Existing EQA benchmarks assume that every question must be answered, but embodied agents should know when they do not have sufficient information to answer. In this work, we focus on a minimal requirement for EQA agents, abstention: knowing when to withhold an answer. From an initial study of 500 human queries, we find that 32.4% contain missing or underspecified context. Drawing on this initial study and cognitive theories of human communication errors, we derive five representative categories requiring abstention: actionability limitation, referential underspecification, preference dependence, information unavailability, and false presupposition. We augment OpenEQA by having annotators transform well-posed questions into ambiguous variants outlined by these categories. The resulting dataset, AbstainEQA, comprises 1,636 annotated abstention cases paired with 1,636 original OpenEQA instances for balanced evaluation. Evaluating on AbstainEQA, we find that even the best frontier model only attains 42.79% abstention recall, while humans achieve 91.17%. We also find that scaling, prompting, and reasoning only yield marginal gains, and that fine-tuned models overfit to textual cues. Together, these results position abstention as a fundamental prerequisite for reliable interaction in embodied settings and as a necessary basis for effective clarification.

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:d531393245b88a8d733421d522169cf9CVF · CVPR 2026

TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning

Wu, Tao, Yang, Li, Zhan, Gen, Zhang, Yabin, Liao, Yiting, Li, Junlin, Fu, Deliang, Zhang, Li 等 9 位作者

原文摘要

Enhancing temporal understanding of MLLMs is essential for long-form video analysis, supporting tasks such as temporal localization and time-sensitive question answering. While reinforcement learning (RL) has been explored for temporal reasoning, existing approaches are often limited to specific tasks or datasets, hindering generalization across diverse temporal scenarios. To address this, we present TempR1, an RL-based method that strengthens MLLMs' temporal comprehension through multi-task training. We construct a multi-task corpus covering diverse temporal structures and build upon the Group Relative Policy Optimization (GRPO) algorithm for stable cross-task optimization. Temporal tasks are categorized into three interval-instance correspondence types, with tailored localization rewards designed for each. This design enables TempR1 to capture fine-grained temporal dependencies and adapt to different temporal patterns. Experiments show that TempR1 achieves state-of-the-art performance on multiple benchmarks. Joint optimization across complementary tasks further produces strong synergy, improving both generalization and single-task performance, thereby providing a scalable paradigm for temporal reasoning in MLLMs.

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 ↗