SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks

Understand

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar.

• These avatars are driven by pose parameters and have realistic clothing that moves and deforms naturally.
• SCANimate does not rely on a customized mesh template or surface mesh registration.
• We observe that fitting a parametric 3D body model, like SMPL, to a clothed human scan is tractable while surface registration of the body topology to the scan is often not, because clothing can deviate significantly from the body shape.