DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks

Understand

Deep learning greatly improved the realism of animatable human models by learning geometry and appearance from collections of 3D scans, template meshes, and multi-view imagery.

• High-resolution models enable photo-realistic avatars but at the cost of requiring studio settings not available to end users.
• Our goal is to create avatars directly from raw images without relying on expensive studio setups and surface tracking.
• While a few such approaches exist, those have limited generalization capabilities and are prone to learning spurious (chance) correlations between irrelevant body parts, resulting in implausible deformations and missing body parts on unseen poses.