4 papers
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer
Yu Deng, Duomin Wang, Baoyuan Wang
In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocul…
AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents
Duomin Wang, Bin Dai, Yu Deng +1
In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspe…
Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data
Yu Deng, Duomin Wang, Xiaohang Ren +2
Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction, yet the latter is evenly challenging which restricts them from…