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The recent advancements in image-text diffusion models have stimulatedresearch interest in large-scale 3D generative models. Nevertheless, thelimited availability of diverse 3D resources presents significant challenges tolearning. In this paper, we present a novel method for generating high-quality,stylized 3D avatars that utilizes pre-trained image-text diffusion models fordata generation and a Generative Adversarial Network (GAN)-based 3D generationnetwork for training. Our method leverages the comprehensive priors ofappearance and geometry offered by image-text diffusion models to generatemulti-view images of avatars in various styles. During data generation, weemploy poses extracted from existing 3D models to guide the generation ofmulti-view images. To address the misalignment between poses and images indata, we investigate view-specific prompts and develop a coarse-to-finediscriminator for GAN training. We also delve into attribute-related prompts toincrease the diversity of the generated avatars. Additionally, we develop alatent diffusion model within the style space of StyleGAN to enable thegeneration of avatars based on image inputs. Our approach demonstrates superiorperformance over current state-of-the-art methods in terms of visual qualityand diversity of the produced avatars.

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation