Structured Local Radiance Fields for Human Avatar Modeling

Abstract

It is extremely challenging to create an animatable clothed human avatar from RGB videos, especially for loose clothes due to the difficulties in motion modeling. To address this problem, we introduce a novel representation on the basis of recent neural scene rendering techniques. The core of our representation is a set of structured local radiance fields, which are anchored to the pre-defined nodes sampled on a statistical human body template. These local radiance fields not only leverage the flexibility of implicit representation in shape and appearance modeling, but also factorize cloth deformations into skeleton motions, node residual translations and the dynamic detail variations inside each individual radiance field. To learn our representation from RGB data and facilitate pose generalization, we propose to learn the node translations and the detail variations in a conditional generative latent space. Overall, our method enables automatic construction of animatable human avatars for various types of clothes without the need for scanning subject-specific templates, and can generate realistic images with dynamic details for novel poses. Experiment show that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

Learning Neural Volumetric Representations of Dynamic Humans in Minutes

“…Recently, some methods [9,46,69,86] model the shapes of dynamic humans as implicit neural representations and attempt to optimize them from human scans. Another line of works [28,30,34,36,40,56,58,65,91,92,94,[100][101][102]104] exploits dynamic implicit neural representations and differentiable renderers to reconstruct 3D human models from videos. To represent dynamic humans, Neural Actor [40] augments the neural radiance field with the linear blend skinning model [35].…”