AniPixel: Towards Animatable Pixel-Aligned Human Avatar

Neural radiance field using pixel-aligned features can render photo-realistic novel views. However, when pixel-aligned features are directly introduced to human avatar reconstruction, the rendering can only be conducted for still humans, rather than animatable avatars. 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. Moreover, we devise a pose-dependent and view direction-related shading module to represent the local illumination variance. Experiments show that our AniPixel renders comparable novel views while delivering better novel pose animation results than state-of-the-art methods. The code will be released.