  Recent techniques on implicit geometry representation learning and neural
rendering have shown promising results for 3D clothed human reconstruction from
sparse video inputs. However, it is still challenging to reconstruct detailed
surface geometry and even more difficult to synthesize photorealistic novel
views with animated human poses. In this work, we introduce PGAHum, a
prior-guided geometry and appearance learning framework for high-fidelity
animatable human reconstruction. We thoroughly exploit 3D human priors in three
key modules of PGAHum to achieve high-quality geometry reconstruction with
intricate details and photorealistic view synthesis on unseen poses. First, a
prior-based implicit geometry representation of 3D human, which contains a
delta SDF predicted by a tri-plane network and a base SDF derived from the
prior SMPL model, is proposed to model the surface details and the body shape
in a disentangled manner. Second, we introduce a novel prior-guided sampling
strategy that fully leverages the prior information of the human pose and body
to sample the query points within or near the body surface. By avoiding
unnecessary learning in the empty 3D space, the neural rendering can recover
more appearance details. Last, we propose a novel iterative backward
deformation strategy to progressively find the correspondence for the query
point in observation space. A skinning weights prediction model is learned
based on the prior provided by the SMPL model to achieve the iterative backward
LBS deformation. Extensive quantitative and qualitative comparisons on various
datasets are conducted and the results demonstrate the superiority of our
framework. Ablation studies also verify the effectiveness of each scheme for
geometry and appearance learning.
