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Superman framework unifies human motion perception and generation

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Superman framework unifies human motion perception and generation

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

  1. arXiv cs.CV TIER_1 English(EN) · Xinshun Wang, Peiming Li, Ziyi Wang, Zhongbin Fang, Zhichao Deng, Songtao Wu, Jason Li, Mengyuan Liu ·

    Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation

    arXiv:2602.02401v2 Announce Type: replace Abstract: 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 fi…

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