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

arXiv: https://arxiv.org/abs/2305.19012 · HTML: https://mlsanity.com/paper/2305.19012/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 48 of its 63 references that are in the map.
- First posted on arXiv 30 May 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 48)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)

## Built on by (most cited first, 25 of 25)

- [CityDreamer: Compositional Generative Model of Unbounded 3D Cities](https://mlsanity.com/paper/2309.00610/) (2023)
- [DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars](https://mlsanity.com/paper/2311.18635/) (2023)
- [LLMs Meet Multimodal Generation and Editing: A Survey](https://mlsanity.com/paper/2405.19334/) (2024)
- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [CityCraft: A Real Crafter for 3D City Generation](https://mlsanity.com/paper/2406.04983/) (2024)
- [HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation](https://mlsanity.com/paper/2312.07539/) (2023)
- [Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior](https://mlsanity.com/paper/2403.09140/) (2024)
- [DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors](https://mlsanity.com/paper/2312.16837/) (2023)
- [AvatarArtist: Open-Domain 4D Avatarization](https://mlsanity.com/paper/2503.19906/) (2025)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [HeadEvolver: Text to Head Avatars via Expressive and Attribute-Preserving Mesh Deformation](https://mlsanity.com/paper/2403.09326/) (2024)
- [ET3D: Efficient Text-to-3D Generation via Multi-View Distillation](https://mlsanity.com/paper/2311.15561/) (2023)
- [BodyShapeGPT: SMPL Body Shape Manipulation with LLMs](https://mlsanity.com/paper/2410.03556/) (2024)
- [WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction](https://mlsanity.com/paper/2502.01045/) (2025)
- [LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example](https://mlsanity.com/paper/2403.15227/) (2024)
- [GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D Generators](https://mlsanity.com/paper/2412.15491/) (2024)
- [One-shot Generative Domain Adaptation in 3D GANs](https://mlsanity.com/paper/2410.08824/) (2024)
- [AniArtAvatar: Animatable 3D Art Avatar from a Single Image](https://mlsanity.com/paper/2403.17631/) (2024)
- [Revealing Directions for Text-guided 3D Face Editing](https://mlsanity.com/paper/2410.04965/) (2024)
- [StyleDiT: A Unified Framework for Diverse Child and Partner Faces Synthesis with Style Latent Diffusion Transformer](https://mlsanity.com/paper/2412.10785/) (2024)
- [Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars](https://mlsanity.com/paper/2503.11978/) (2025)
- [Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information](https://mlsanity.com/paper/2403.15048/) (2024)
- [SceneX: Procedural Controllable Large-scale Scene Generation](https://mlsanity.com/paper/2403.15698/) (2024)
- [StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image Translation](https://mlsanity.com/paper/2508.11203/) (2025)
- [Exploring Disentangled and Controllable Human Image Synthesis: From End-to-End to Stage-by-Stage](https://mlsanity.com/paper/2503.19486/) (2025)

## Abstract (arXiv)

The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text diffusion models for data generation and a Generative Adversarial Network (GAN)-based 3D generation network for training. Our method leverages the comprehensive priors of appearance and geometry offered by image-text diffusion models to generate multi-view images of avatars in various styles. During data generation, we employ poses extracted from existing 3D models to guide the generation of multi-view images. To address the misalignment between poses and images in data, we investigate view-specific prompts and develop a coarse-to-fine discriminator for GAN training. We also delve into attribute-related prompts to increase the diversity of the generated avatars. Additionally, we develop a latent diffusion model within the style space of StyleGAN to enable the generation of avatars based on image inputs. Our approach demonstrates superior performance over current state-of-the-art methods in terms of visual quality and diversity of the produced avatars.