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.

我们介绍了 PGAHum，一种先验引导的几何与外观学习框架，用于高保真度可动人体重建。我们在三个关键模块中充分利用 3D 人体先验，实现了具有细节丰富的几何重建和能在未见过的姿势上合成逼真视图的高质量结果。

PGAHum：先验引导的几何与外观学习用于高保真可动人物重建

PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity  Animatable Human Reconstruction

We introduce Plenoxels (plenoptic voxels), a system for photorealistic view
synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical
harmonics. This representation can be optimized from calibrated images via
gradient methods and regularization without any neural components. On standard,
benchmark tasks, Plenoxels are optimized two orders of magnitude faster than
Neural Radiance Fields with no loss in visual quality.

Plenoxels 是一种用于光线追踪的系统，将场景表示为一个稀疏的 3D 网格模型，并通过梯度方法和正则化优化，能够实现与神经辐射场相当的质量，却快 100 倍。