FuRPE: Learning Full-body Reconstruction from Part Experts

Understand

In the field of full-body reconstruction, the scarcity of annotated data often impedes the efficacy of prevailing methods.

• To address this issue, we introduce FuRPE, a novel framework that employs part-experts and an ingenious pseudo ground-truth selection scheme to derive high-quality pseudo labels.
• These labels, central to our approach, equip our network with the capability to efficiently learn from the available data.
• Integral to FuRPE is a unique exponential moving average training strategy and expert-derived feature distillation strategy.