  In this paper, we propose a novel framework, Tracking-free Relightable Avatar
(TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared
to previous methods, TRAvatar works in a more practical and efficient setting.
Specifically, TRAvatar is trained with dynamic image sequences captured in a
Light Stage under varying lighting conditions, enabling realistic relighting
and real-time animation for avatars in diverse scenes. Additionally, TRAvatar
allows for tracking-free avatar capture and obviates the need for accurate
surface tracking under varying illumination conditions. Our contributions are
two-fold: First, we propose a novel network architecture that explicitly builds
on and ensures the satisfaction of the linear nature of lighting. Trained on
simple group light captures, TRAvatar can predict the appearance in real-time
with a single forward pass, achieving high-quality relighting effects under
illuminations of arbitrary environment maps. Second, we jointly optimize the
facial geometry and relightable appearance from scratch based on image
sequences, where the tracking is implicitly learned. This tracking-free
approach brings robustness for establishing temporal correspondences between
frames under different lighting conditions. Extensive qualitative and
quantitative experiments demonstrate that our framework achieves superior
performance for photorealistic avatar animation and relighting.
