UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with
  Authenticity Guided Textures
Mingyuan Zhou Rakib Hyder Ziwei Xuan Guojun Qi
Recent advances in 3D avatar generation have gained significant attentions.
These breakthroughs aim to produce more realistic animatable avatars, narrowing
the gap between virtual and real-world experiences. Most of existing works
employ Score Distillation Sampling (SDS) loss, combined with a differentiable
renderer and text condition, to guide a diffusion model in generating 3D
avatars. However, SDS often generates oversmoothed results with few facial
details, thereby lacking the diversity compared with ancestral sampling. On the
other hand, other works generate 3D avatar from a single image, where the
challenges of unwanted lighting effects, perspective views, and inferior image
quality make them difficult to reliably reconstruct the 3D face meshes with the
aligned complete textures. In this paper, we propose a novel 3D avatar
generation approach termed UltrAvatar with enhanced fidelity of geometry, and
superior quality of physically based rendering (PBR) textures without unwanted
lighting. To this end, the proposed approach presents a diffuse color
extraction model and an authenticity guided texture diffusion model. The former
removes the unwanted lighting effects to reveal true diffuse colors so that the
generated avatars can be rendered under various lighting conditions. The latter
follows two gradient-based guidances for generating PBR textures to render
diverse face-identity features and details better aligning with 3D mesh
geometry. We demonstrate the effectiveness and robustness of the proposed
method, outperforming the state-of-the-art methods by a large margin in the
experiments.