One Shot, One Talk: Whole-body Talking Avatar from a Single Image
Authors: name: Jun Xiang, name: Yudong Guo, name: Leipeng Hu, name: Boyang Guo, name: Yancheng Yuan, name: Juyong Zhang

Building realistic and animatable avatars still requires minutes of
multi-view or monocular self-rotating videos, and most methods lack precise
control over gestures and expressions. To push this boundary, we address the
challenge of constructing a whole-body talking avatar from a single image. We
propose a novel pipeline that tackles two critical issues: 1) complex dynamic
modeling and 2) generalization to novel gestures and expressions. To achieve
seamless generalization, we leverage recent pose-guided image-to-video
diffusion models to generate imperfect video frames as pseudo-labels. To
overcome the dynamic modeling challenge posed by inconsistent and noisy
pseudo-videos, we introduce a tightly coupled 3DGS-mesh hybrid avatar
representation and apply several key regularizations to mitigate
inconsistencies caused by imperfect labels. Extensive experiments on diverse
subjects demonstrate that our method enables the creation of a photorealistic,
precisely animatable, and expressive whole-body talking avatar from just a
single image.