At the core of portrait photography is the search for ideal lighting and
viewpoint. The process often requires advanced knowledge in photography and an
elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric
relighting method that is capable of synthesizing novel viewpoints, and novel
lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN
(EG3D) to reconstruct geometry and appearance from an input portrait as a set
of 3D-aware features. We design a relighting module conditioned on a given
lighting to process these features, and predict a relit 3D representation in
the form of a tri-plane, which can render to an arbitrary viewpoint through
volume rendering. Besides viewpoint and lighting control, Holo-Relighting also
takes the head pose as a condition to enable head-pose-dependent lighting
effects. With these novel designs, Holo-Relighting can generate complex
non-Lambertian lighting effects (e.g., specular highlights and cast shadows)
without using any explicit physical lighting priors. We train Holo-Relighting
with data captured with a light stage, and propose two data-rendering
techniques to improve the data quality for training the volumetric relighting
system. Through quantitative and qualitative experiments, we demonstrate
Holo-Relighting can achieve state-of-the-arts relighting quality with better
photorealism, 3D consistency and controllability.

Holo-Relighting 是一种能够从单张图像中合成新视角和新光照的体积照明方法，利用预训练的 3D GAN 来重建几何和外观，并通过体积渲染将其渲染到任意观点。通过图像渲染和质量提升技术，Holo-Relighting 可以实现更好的光照效果、3D 一致性和可控性，从而达到最先进的照明质量。

全息照明：从单张图像实现可控体积肖像照明

Holo-Relighting: Controllable Volumetric Portrait Relighting from a  Single Image

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.

提出了一种新颖的 3D 人物生成方法 UltrAvatar，通过增强几何保真度和质感材质的质量来生成更真实且具有物理基础渲染（PBR）效果的 3D 人物模型，克服了光照影响和获得更多面部细节的挑战，并在实验中展示了该方法的有效性和鲁棒性。