  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. These novel elements of FuRPE not only serve to further
refine the model but also to reduce potential biases that may arise from
inaccuracies in pseudo labels, thereby optimizing the network's training
process and enhancing the robustness of the model. We apply FuRPE to train both
two-stage and fully convolutional single-stage full-body reconstruction
networks. Our exhaustive experiments on numerous benchmark datasets illustrate
a substantial performance boost over existing methods, underscoring FuRPE's
potential to reshape the state-of-the-art in full-body reconstruction.
