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首次观测 · 记录 #1CVF · CVPR 2026:cvf:048e661cb570489152a6a107b58ab1e4CVF · CVPR 2026

JoPPO: Hierarchical Photography Assessment via Contrastive Joint Conditional Probabilistic Reinforcement Learning

Yang, Yifan, Wang, Juntuo, Qiao, Yuming, Zhang, Xudong, Yu, Chunyang, Li, Yan, Lin, Xiao, Luo, Liang 等 9 位作者

原文摘要

With the advancement of Vision-Language Models (VLMs), employing VLM-as-a-Judge for visual evaluation has become a widely adopted metric in vision research. However, existing VLM-as-a-Judge approaches suffer from biased scoring outcomes with low discrimination and lack the capacity for unified multi-attribute compositional assessment. To address these limitations, we propose a novel training paradigm, termed JoPPO (**Jo**int **P**robabilistic **P**olicy **O**ptimization) that enables the VLMs to learn ranking under compositional assessment constraints. We evaluate the JoPPO on image aesthetics as a testbed, a task requiring nuanced understanding of multiple attributes including composition, lighting, color and geometry. Training follows two stages: (1) Supervised Fine-Tuning (SFT) on synthetic composition dataset provided by automated data generation pipeline to instill compositional priors; and (2) Contrastive Joint Conditional Probabilistic Reinforcement Learning: building upon the GRPO algorithm, we introduce JoPPO, which compute reward based on the expected win rate of total scores derived from the conditional distribution of fine-grained attribute scores within batches, effectively enhancing the model's discriminative ability in composite evaluation. Across standard aesthetic benchmarks, our method achieves consistent improvements in ranking consistency, demonstrating strong zero-shot generalization.

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:38977121665bf616602fcb2cf0daa9ebCVF · CVPR 2026

EagleVision: A Dual-Stage Framework with BEV-grounding-based Chain-of-Thought for Spatial Intelligence

Wan, Jiaxu, Wang, Xu, Xie, Mengwei, Zhang, Hang, Xu, Mu, Han, Yang, Yuan, Ding, Zhang, Hong 等 9 位作者

原文摘要

Video-based spatial reasoning -- such as estimating distances, judging directions, or understanding layouts from multiple views -- requires selecting informative frames and, when needed, actively seeking additional viewpoints during inference. Existing multimodal large language models (MLLMs) consume a fixed set of uniformly sampled frames and cannot request new views once reasoning begins, often missing the geometric cues necessary for reliable spatial judgments. We present EagleVision, a dual-stage framework that combines geometry-aware frame selection with active, Bird's-Eye-View (BEV)-grounded reasoning. In the first stage (macro perception), a semantics-perspective-fusion determinantal point process (SPF-DPP) selects a compact set of keyframes that jointly maximize semantic relevance and viewpoint diversity under a fixed token budget. In the second stage (micro verification), the model performs iterative spatial Chain-of-Thought: at each step it can either reason in text or predict a pose on the BEV plane to retrieve the nearest real frame, forming a closed-loop hypothesize-look-verify cycle. The querying policy is trained purely via reinforcement learning with a spatial grounding reward, requiring no human-annotated reasoning traces. On VSI-Bench and SQA3D, EagleVision achieves state-of-the-art performance among open-source vision-language models.

cs.CV · cs.CL

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3aeaacfa76d784133cc43855aa457c77CVF · CVPR 2026

Region-Adaptive Sampling for Diffusion Transformers

Liu, Ziming, Yang, Yifan, Zhang, Chengruidong, Zhang, Yiqi, Qiu, Lili, You, Yang, Yang, Yuqing

原文摘要

Diffusion models (DMs) have become the state-of-the-art for generative tasks across domains, but their reliance on sequential forward passes limits real-time performance. Prior acceleration methods mainly reduce sampling steps or reuse intermediate results. Leveraging the flexibility of Diffusion Transformers (DiTs) to handle variable token counts, we propose RAS, a training-free sampling strategy that dynamically assigns different update ratios to image regions based on model focus. Our key observation is that at each step, DiTs concentrate on semantically meaningful areas, and these regions exhibit strong continuity across consecutive steps. Exploiting this, RAS updates only focused regions while reusing cached noise for others, with focus determined from the previous step's output. Evaluated on Stable Diffusion 3 and Lumina-Next-T2I, RAS achieves up to 2.36x and 2.51x speedups, respectively, with minimal quality loss. This demonstrates a practical step toward more efficient diffusion transformers for real-time 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:6f649a3fdeafdd8fe43558e0089ba8e2CVF · CVPR 2026

Tavatar: Topology-Aware Gaussian Attribute Derivation for Animatable Human Avatars

Luo, Hailin, Yang, Yifan, Shu, Jiazhi, Huang, Zixiong, Chen, Qi, Du, Qing, Tan, Mingkui

原文摘要

Reconstructing high-fidelity, animatable human avatars from monocular videos remains a critical challenge. Existing 3DGS-based human animation methods constrain Gaussian parameters but exclude scale, which we argue is crucial for adapting human poses to challenging out-of-distribution poses. To achieve robust animation under unseen poses, we propose Tavatar, which derives key parameters such as scale, rotation, and other geometric attributes directly from the local mesh geometry, instead of learning them through unconstrained optimization. This paradigm shift enforces topological consistency by design, as each Gaussian is analytically anchored to the local mesh geometry, inheriting its spatial structure and deformation behavior. Specifically, we bind Gaussians to mesh faces and vertices, deriving their scales and orientations from triangle properties and local edge lengths to ensure coherent surface coverage. To ensure the stability of this analytical mapping, we introduce a crucial equilateral regularization term that preserves mesh integrity. Extensive experiments demonstrate that Tavatar achieves superior animation robustness on challenging out-of-distribution poses, reducing normal error by 13.8% on X-Avatar and 17.9% on PeopleSnapshot against the best baseline, while maintaining competitive rendering quality.

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 ↗