  Although PIFu-based 3D human reconstruction methods are popular, the quality
of recovered details is still unsatisfactory. In a sparse (e.g., 3 RGBD
sensors) capture setting, the depth noise is typically amplified in the PIFu
representation, resulting in flat facial surfaces and geometry-fallible bodies.
In this paper, we propose a novel geometry-aware two-scale PIFu for 3D human
reconstruction from sparse, noisy inputs. Our key idea is to exploit the
complementary properties of depth denoising and 3D reconstruction, for learning
a two-scale PIFu representation to reconstruct high-frequency facial details
and consistent bodies separately. To this end, we first formulate depth
denoising and 3D reconstruction as a multi-task learning problem. The depth
denoising process enriches the local geometry information of the reconstruction
features, while the reconstruction process enhances depth denoising with global
topology information. We then propose to learn the two-scale PIFu
representation using two MLPs based on the denoised depth and geometry-aware
features. Extensive experiments demonstrate the effectiveness of our approach
in reconstructing facial details and bodies of different poses and its
superiority over state-of-the-art methods.
