GPAvatar: Generalizable and Precise Head Avatar from Image(s)
Xuangeng Chu Yu Li Ailing Zeng Tianyu Yang Lijian Lin Yunfei Liu Tatsuya Harada
Head avatar reconstruction, crucial for applications in virtual reality,
online meetings, gaming, and film industries, has garnered substantial
attention within the computer vision community. The fundamental objective of
this field is to faithfully recreate the head avatar and precisely control
expressions and postures. Existing methods, categorized into 2D-based warping,
mesh-based, and neural rendering approaches, present challenges in maintaining
multi-view consistency, incorporating non-facial information, and generalizing
to new identities. In this paper, we propose a framework named GPAvatar that
reconstructs 3D head avatars from one or several images in a single forward
pass. The key idea of this work is to introduce a dynamic point-based
expression field driven by a point cloud to precisely and effectively capture
expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion
module in the tri-planes canonical field to leverage information from multiple
input images. The proposed method achieves faithful identity reconstruction,
precise expression control, and multi-view consistency, demonstrating promising
results for free-viewpoint rendering and novel view synthesis.