  Although human reconstruction typically results in human-specific avatars,
recent 3D scene reconstruction techniques utilizing pixel-aligned features show
promise in generalizing to new scenes. Applying these techniques to human
avatar reconstruction can result in a volumetric avatar with generalizability
but limited animatability due to rendering only being possible for static
representations. In this paper, we propose AniPixel, a novel animatable and
generalizable human avatar reconstruction method that leverages pixel-aligned
features for body geometry prediction and RGB color blending. Technically, to
align the canonical space with the target space and the observation space, we
propose a bidirectional neural skinning field based on skeleton-driven
deformation to establish the target-to-canonical and canonical-to-observation
correspondences. Then, we disentangle the canonical body geometry into a
normalized neutral-sized body and a subject-specific residual for better
generalizability. As the geometry and appearance are closely related, we
introduce pixel-aligned features to facilitate the body geometry prediction and
detailed surface normals to reinforce the RGB color blending. We also devise a
pose-dependent and view direction-related shading module to represent the local
illumination variance. Experiments show that AniPixel renders comparable novel
views while delivering better novel pose animation results than
state-of-the-art methods.
