Superman framework unifies human motion perception and generation

Researchers have introduced Superman, a novel framework designed to unify human motion perception and generation tasks. This system bridges the gap between understanding motion from video and generating temporal skeleton-based movements. Superman utilizes a Vision-Guided Motion Tokenizer to create a unified, cross-modal motion vocabulary from both visual data and 3D skeletons. A single MLLM architecture then handles diverse tasks, including 3D pose estimation from video, and skeleton-based motion prediction and in-betweening, achieving state-of-the-art results on benchmarks like Human3.6M. AI

IMPACT This framework could streamline research and development in human motion analysis by unifying perception and generation tasks.

RANK_REASON This is a research paper describing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• Human3.6M
• Skinned Multi Person Linear Model
• Xinshun Wang

• paper
• model release

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