UltrAvatar：一個具有真實動畫效果的3D頭像擴散模型，並帶有真實感引導紋理

UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures

January 20, 2024

作者: Mingyuan Zhou, Rakib Hyder, Ziwei Xuan, Guojun Qi

cs.AI

摘要

近年來，3D角色生成的最新進展受到了相當大的關注。這些突破旨在產生更加逼真且可動的角色，縮小虛擬與現實世界體驗之間的差距。大多數現有作品採用得分蒸餾取樣（Score Distillation Sampling，SDS）損失，結合可微分渲染器和文本條件，引導擴散模型生成3D角色。然而，SDS通常會生成過度平滑的結果，臉部細節較少，因此與祖先取樣相比缺乏多樣性。另一方面，其他作品從單張圖像生成3D角色，其中不需要的光線效果、透視視角和較低的圖像質量等挑戰使它們難以可靠地重建具有對齊完整紋理的3D面部網格。在本文中，我們提出了一種名為UltrAvatar的新型3D角色生成方法，具有增強的幾何保真度和優質的基於物理的渲染（PBR）紋理，並且沒有不需要的光線效果。為此，所提出的方法提出了一種擴散色彩提取模型和一種真實性引導的紋理擴散模型。前者消除了不需要的光線效果，顯示真實的擴散色彩，使生成的角色可以在各種照明條件下渲染。後者遵循兩種基於梯度的引導，用於生成PBR紋理，以更好地呈現多樣的面部特徵和與3D網格幾何更好對齊的細節。我們展示了所提出方法的有效性和魯棒性，在實驗中大幅優於最先進的方法。

English

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.