超人：统一骨骼与视觉用于人体运动感知与生成
Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation
AI总结：
针对人体运动分析范式碎片化问题，提出Superman统一框架，通过视觉引导运动分词器创建跨模态运动词汇，训练单一MLLM架构处理多任务，实验表明该方法在运动任务中性能领先或具竞争力，为运动分析提供高效可扩展路径。
摘要：
人体运动分析任务在计算机视觉中至关重要，但当前范式存在严重碎片化问题。如感知模型与生成模型分离，生成式MLLMs限于单帧静态姿势，现有运动词汇仅基于骨骼数据构建。为此提出Superman统一框架，通过视觉引导运动分词器克服模态脱节，创建统一跨模态运动词汇，在此基础上训练单一统一MLLM架构处理所有任务。在包括Human3.6M等标准基准上的大量实验表明，该统一方法在所有运动任务中实现了领先或有竞争力的性能，为基于骨骼的生成式运动分析提供了更高效可扩展的路径。
英文摘要：
Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vision. However, current paradigms suffer from severe fragmentation. First, the field is split between ``perception'' models that understand motion from video but only output text, and ``generation'' models that cannot perceive from raw visual input. Second, generative MLLMs are often limited to single-frame, static poses using dense, parametric SMPL models, failing to handle temporal motion. Third, existing motion vocabularies are built from skeleton data alone, severing the link to the visual domain. To address these challenges, we introduce Superman, a unified framework that bridges visual perception with temporal, skeleton-based motion generation. Our solution is twofold. First, to overcome the modality disconnect, we propose a Vision-Guided Motion Tokenizer. Leveraging the natural geometric alignment between 3D skeletons and visual data, this module pioneers robust joint learning from both modalities, creating a unified, cross-modal motion vocabulary. Second, grounded in this motion language, a single, unified MLLM architecture is trained to handle all tasks. This module flexibly processes diverse, temporal inputs, unifying 3D skeleton pose estimation from video (perception) with skeleton-based motion prediction and in-betweening (generation). Extensive experiments on standard benchmarks, including Human3.6M, demonstrate that our unified method achieves state-of-the-art or competitive performance across all motion tasks. This showcases a more efficient and scalable path for generative motion analysis using skeletons.
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