Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures

Understand

Human pose transfer synthesizes new view(s) of a person for a given pose.

• Recent work achieves this via self-reconstruction, which disentangles a person's pose and texture information by breaking the person down into parts, then recombines them for reconstruction.
• However, part-level disentanglement preserves some pose information that can create unwanted artifacts.
• In this paper, we propose Pose Transfer by Permuting Textures (PT$^2$), an approach for self-driven human pose transfer that disentangles pose from texture at the patch-level.