基于渐进式潜空间优化的文本驱动多样化面部纹理生成
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement
AI总结：
针对现有3D面部纹理生成方法的局限，提出渐进式潜空间优化方法，结合自监督GAN与边缘感知SDS的互增强机制，生成高质量多样化的PBR面部纹理，性能优于现有方法。
摘要：
自动3D面部纹理生成近年来受到广泛关注。现有方法可能不支持传统的基于物理的渲染管线，或是依赖Light Stage采集的3D数据。我们的核心贡献是提出一种渐进式潜空间优化方法，能够从由人脸图像生成的、基于3D Morphable Models（3DMM，三维可变形模型）的纹理图启动，生成高质量且多样化的PBR（基于物理的渲染）纹理，包含反照率、法线和粗糙度图。该方法首先增强生成对抗网络（GANs）以实现文本引导的多样化纹理生成。为此，我们设计了一种自监督范式，以摆脱对真值3D纹理的依赖，仅使用纠缠的纹理图训练生成模型。此外，我们推动GANs与Score Distillation Sampling（SDS，分数蒸馏采样）实现相互增强：SDS为GANs带来更多生成模式，GANs则提升SDS的优化效率。我们还引入了边缘感知SDS以实现多视角一致的面部结构。实验表明，我们的方法在照片级真实感质量、多样性和效率方面均优于现有3D纹理生成方法。
英文摘要：
Automatic 3D facial texture generation has gained significant interest recently. Existing approaches may not support the traditional physically based rendering pipeline or rely on 3D data captured by Light Stage. Our key contribution is a progressive latent space refinement approach that can bootstrap from 3D Morphable Models (3DMMs)-based texture maps generated from facial images to generate high-quality and diverse PBR textures, including albedo, normal, and roughness. It starts with enhancing Generative Adversarial Networks (GANs) for text-guided and diverse texture generation. To this end, we design a self-supervised paradigm to overcome the reliance on ground truth 3D textures and train the generative model with only entangled texture maps. Besides, we foster mutual enhancement between GANs and Score Distillation Sampling (SDS). SDS boosts GANs with more generative modes, while GANs promote more efficient optimization of SDS. Furthermore, we introduce an edge-aware SDS for multi-view consistent facial structure. Experiments demonstrate that our method outperforms existing 3D texture generation methods regarding photo-realistic quality, diversity, and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
