# AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation

arXiv: https://arxiv.org/abs/2306.09864 · HTML: https://mlsanity.com/paper/2306.09864/

## Key facts

- Cited by 37 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 55 of its 64 references that are in the map.
- First posted on arXiv 16 Jun 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 55)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [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)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [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)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion](https://mlsanity.com/paper/2212.06135/) (2022)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)

## Built on by (most cited first, 30 of 37)

- [TeCH: Text-guided Reconstruction of Lifelike Clothed Humans](https://mlsanity.com/paper/2308.08545/) (2023)
- [HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting](https://mlsanity.com/paper/2311.17061/) (2023)
- [HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation](https://mlsanity.com/paper/2310.01406/) (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)
- [Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation](https://mlsanity.com/paper/2403.09625/) (2024)
- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation](https://mlsanity.com/paper/2410.07164/) (2024)
- [SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance](https://mlsanity.com/paper/2312.08889/) (2023)
- [HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation](https://mlsanity.com/paper/2312.07539/) (2023)
- [PuzzleAvatar: Assembling 3D Avatars from Personal Albums](https://mlsanity.com/paper/2405.14869/) (2024)
- [TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation](https://mlsanity.com/paper/2403.12906/) (2024)
- [A Survey on 3D Human Avatar Modeling -- From Reconstruction to Generation](https://mlsanity.com/paper/2406.04253/) (2024)
- [STAR: Skeleton-aware Text-based 4D Avatar Generation with In-Network Motion Retargeting](https://mlsanity.com/paper/2406.04629/) (2024)
- [Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation](https://mlsanity.com/paper/2412.14453/) (2024)
- [UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections](https://mlsanity.com/paper/2509.24817/) (2025)
- [ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians](https://mlsanity.com/paper/2406.16815/) (2024)
- [SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing](https://mlsanity.com/paper/2412.09545/) (2024)
- [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)
- [X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation](https://mlsanity.com/paper/2405.00954/) (2024)
- [ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling](https://mlsanity.com/paper/2405.16570/) (2024)
- [TeRA: Rethinking Text-guided Realistic 3D Avatar Generation](https://mlsanity.com/paper/2509.02466/) (2025)
- [MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space](https://mlsanity.com/paper/2404.01296/) (2024)
- [Directional Texture Editing for 3D Models](https://mlsanity.com/paper/2309.14872/) (2023)
- [PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation](https://mlsanity.com/paper/2310.09458/) (2023)
- [Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting](https://mlsanity.com/paper/2504.20403/) (2025)
- [SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation](https://mlsanity.com/paper/2505.05475/) (2025)
- [Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies](https://mlsanity.com/paper/2507.10029/) (2025)
- [HART: Human Aligned Reconstruction Transformer](https://mlsanity.com/paper/2509.26621/) (2025)

## Abstract (arXiv)

We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.