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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6ffb0cc38d5e30a937fa9cc5a9a91ba6CVF · CVPR 2026

HSI-GPT2: A Dual-Granularity Large Motion Reasoning Model with Diffusion Refinement for Human-Scene Interaction

Wang, Yuan, Li, Xiang, Li, Yali, Hou, Xuege, Wang, Shengjin

原文摘要

Unified interpreting and synthesizing human behaviors within 3D environments is vital for advancing spatial intelligence and humanoid robotics. Despite recent advancements (e.g., HSI-GPT), two fundamental capabilities expected of a unified model--understanding and generation--still lag behind specialist models. This is primarily due to 1) single-granularity codebook overemphasizes low-frequency motion details while neglecting motion semantics, 2) limited decoding capacity of the motion detokenizer which restricts the fidelity of human-scene interactions, 3) only relying on supervised fine-tuning (SFT) failing to capture high-level motion semantics and logical reasoning with an end-to-end mapping. To this issue, we develop HSI-GPT2--a reasoning-enhanced, dual-granularity motion-representational large Scene-Motion-Language model, powered by reinforcement learning (RL) with Chain-of-Thought (CoT) reasoning. First, HSI-GPT2 introduces a Dual-granularity Motion Tokenizer, DMoTok, which jointly preserves both fine-grained motion details and text-aligned motion semantics for various HSI-related tasks. Further, a motion diffusion decoder functions as a motion detokenizer, translating deep semantics and detailed features of LLMs into physically grounded human motions. Finally, we curate a Motion Chain-of-Thought (MoCoT) data engine and extend a Group Relative Policy Optimization (GRPO) paradigm to execute long-horizon and compositionallyrich commands. Results on standard HSI benchmarks confirm the clear superiority of HSI-GPT2 in enhancing interaction quality, semantic alignment, behavioral diversity, and generalization to unseen 3D scenes.

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:95605eaa1f956c4092b3f77a35e65249CVF · CVPR 2026

TRM-VLA: Temporal-Aware Chain-of-Thought Reasoning and Memorization for Vision-Language-Action Models

Li, Xiang, Li, Ya-Li, Wang, Yuan, Wang, Shengjin

原文摘要

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general robotic manipulation. However, existing approaches typically omit intermediate reasoning steps and directly regress actions, limiting reasoning interpretability and performance in long-horizon or compositional tasks. Although recent studies introduce Chain-of-Thought (CoT) reasoning into VLA models, their effectiveness remains suboptimal due to two key issues: (1) generating a full reasoning trajectory at every timestep introduces substantial redundancy, thereby hinders real-time deployment and (2) reasoning is performed independently, neglecting temporal consistency, which leads to planning conflicts.We propose TRM-VLA, a temporal-aware reasoning and memorization framework that integrates explicit temporal modeling into the VLA reasoning process. TRM-VLA consists of two core components: (1) Keyframe-Triggered Reasoning (KTR), which identifies task progress and performs hierarchical CoT reasoning only at key decision points to reduce redundant inference; and (2) Granularity-adaptable Context Memory (GCM), which dynamically stores and retrieves historical reasoning trajectories to maintain inter-frame coherence and global context. Built upon a dual-system architecture--combining a multimodal foundation model for slow reasoning (System 2) with a diffusion-based policy for fast execution (System 1)--TRM-VLA learns to plan and act efficiently in a unified manner. Extensive experiments on LIBERO-90, SIMPLER, and four real-world robotic tasks demonstrate that TRM-VLA achieves state-of-the-art performance while improving reasoning efficiency.

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:bdf9bc262ad622f82035d52c2007ca8cCVF · CVPR 2026

VES-RFT: Rewarding Visual Evidence Sensitivity to Mitigate Hallucinations in Large Vision-Language Models

Hou, Xuehe, Li, Wenshuo, Li, Yali, Shu, Han, Wang, Yuan, Chen, Xinghao, Wang, Shengjin

原文摘要

Vision-Language Models (VLMs) often over-rely on linguistic priors even when images are provided, leading to object hallucinations. We revisit object-wise hallucination from the perspective of how visual evidence shapes the model's uncertainty. For each input, we measure decision uncertainty with and without the image, and define a Visual Evidence Sensitivity (VES) signal as the image-attributable change in entropy. Building on this signal, we introduce Visual Evidence Sensitivity Reinforcement Fine-Tuning (VES-RFT), a training-time reinforcement fine-tuning method that explicitly rewards reliance on correct visual evidence. We pair this continuous, annotation-free signal with a verifiable reward that enforces factual object correctness by automatically checking generated object mentions against the image, yielding a computable objective without human annotations. We optimize the dual objective using critic-free GRPO with KL regularization, requiring only parallel image and no-image passes during training while preserving single-pass inference. Across multiple VLM families and benchmarks, VES-RFT consistently suppresses object hallucinations and improves robustness under ambiguity. Specifically, on LLaVA-7B, VES-RFT reduces 12.8 and 1.8 on CHAIR_S/CHAIR_I of MS-COCO, and increases POPE accuracy by 4.92%. Extensive experiments indicate that turning uncertainty into a learnable reward, paired with verifiable correctness signals, provides a scalable mechanism for training-time hallucination mitigation and stronger visual grounding.

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 ↗