[2604.07823] LPM 1.0: Video-based Character Performance Model

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.07823 (cs) [Submitted on 9 Apr 2026 (v1), last revised 14 Apr 2026 (this version, v2)] Title:LPM 1.0: Video-based Character Performance Model Authors:Ailing Zeng, Casper Yang, Chauncey Ge, Eddie Zhang, Garvey Xu, Gavin Lin, Gilbert Gu, Jeremy Pi, Leo Li, Mingyi Shi, Shawn Wang, Sheng Bi, Steven Tang, Thorn Hang, Tobey Guo, Vincent Li, Xin Tong, Yikang Li, Yuchen Sun, Yue Zhao, Yuhan Lu, Yuwei Li, Zane Zhang, Zeshi Yang, Zi Ye View a PDF of the paper titled LPM 1.0: Video-based Character Performance Model, by Ailing Zeng and 24 other authors View PDF HTML (experimental) Abstract:Performance, the externalization of intent, emotion, and personality through visual, vocal, and temporal behavior, is what makes a character alive. Learning such performance from video is a promising alternative to traditional 3D pipelines. However, existing video models struggle to jointly achieve high expressiveness, real-time inference, and long-horizon identity stability, a tension we call the performance trilemma. Conversation is the most comprehensive performance scenario, as characters simultaneously speak, listen, react, and emote while maintaining identity over time. To address this, we present LPM 1.0 (Large Performance Model), focusing on single-person full-duplex audio-visual conversational performance. Concretely, we build a multimodal human-centric dataset through strict filtering, speaking-listening audio-video pair...