HSI-GPT2：面向人-场景交互的双粒度大运动推理模型与扩散精炼
HSI-GPT2: A Dual-Granularity Large Motion Reasoning Model with Diffusion Refinement for Human-Scene Interaction
提出双粒度运动分词器与扩散解码器，将LLM的深层语义转化为物理合理的人体运动，为3D场景中的人-场景交互生成提供了可复用的运动表示与解码方案。
原文依据 a motion diffusion decoder functions as a motion detokenizer, translating deep semantics and detailed features of LLMs into physically grounded human motions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在3D环境中统一解释与合成人类行为对于推进空间智能和人形机器人技术至关重要。尽管近期已有进展（如HSI-GPT），但统一模型所期望的两项基本能力——理解与生成——仍落后于专用模型。这主要归因于：1）单粒度码本过度强调低频运动细节而忽视运动语义；2）运动解分词器的解码能力有限，限制了人-场景交互的保真度；3）仅依赖监督微调（SFT）无法通过端到端映射捕捉高层运动语义和逻辑推理。为解决此问题，我们开发了HSI-GPT2——一种推理增强的双粒度运动表示大场景-运动-语言模型，由强化学习（RL）与思维链（CoT）推理驱动。首先，HSI-GPT2引入双粒度运动分词器DMoTok，针对各类HSI相关任务同时保留细粒度运动细节和文本对齐的运动语义。此外，运动扩散解码器作为运动解分词器，将LLM的深层语义和详细特征转化为物理合理的人体运动。最后，我们构建了运动思维链（MoCoT）数据引擎，并扩展了组相对策略优化（GRPO）范式以执行长时程和组合丰富的指令。在标准HSI基准上的结果证实，HSI-GPT2在提升交互质量、语义对齐、行为多样性以及对未见3D场景的泛化能力方面具有明显优势。
英文原文摘要 1659 字符
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.