  To adequately utilize the available image evidence in multi-view video-based
avatar modeling, we propose TexVocab, a novel avatar representation that
constructs a texture vocabulary and associates body poses with texture maps for
animation. Given multi-view RGB videos, our method initially back-projects all
the available images in the training videos to the posed SMPL surface,
producing texture maps in the SMPL UV domain. Then we construct pairs of human
poses and texture maps to establish a texture vocabulary for encoding dynamic
human appearances under various poses. Unlike the commonly used joint-wise
manner, we further design a body-part-wise encoding strategy to learn the
structural effects of the kinematic chain. Given a driving pose, we query the
pose feature hierarchically by decomposing the pose vector into several body
parts and interpolating the texture features for synthesizing fine-grained
human dynamics. Overall, our method is able to create animatable human avatars
with detailed and dynamic appearances from RGB videos, and the experiments show
that our method outperforms state-of-the-art approaches. The project page can
be found at https://texvocab.github.io/.
