【论文速递】EgoGeneralist: Unifying Egocentric Action-Video Tasks through Video-Native Model

英文标题：EgoGeneralist: Unifying Egocentric Action-Video Tasks through Video-Native Modeling

arXiv编号：2611.08477
作者：Yanlin Jin, Bowen Yang, Cheng Zhang
来源：arXiv
原文链接：https://arxiv.org/abs/2611.08477

中文摘要：
Modern video models possess strong visual priors, yet do not natively represent human action in metric 3D space. We present EgoGeneralist, which adapts pretrained video generators to unify action-conditioned egocentric video generation, pose estimation, and joint action-video generation. Our role-adaptive action renderer provides video-native representations for multidirectional action-video learning, making metric motion usable as both a spatial control signal and a recoverable generation target. Cross-stream action-video coupling establishes close correspondence between metric motion and visual dynamics through lightweight adaptation while preserving the pretrained visual prior. Together, these designs establish a generalist framework spanning five action-video tasks. Despite adaptation only on controlled laboratory recordings, EgoGeneralist generalizes to diverse unseen scenes and interactions with effective action control and visual quality, bridging pretrained visual knowledge and metric 3D human motion.

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