# AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose

arXiv: https://arxiv.org/abs/2308.03610 · HTML: https://mlsanity.com/paper/2308.03610/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 42 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 30 of 34)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [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)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [CLIP-Mesh: Generating textured meshes from text using pretrained image-text models](https://mlsanity.com/paper/2203.13333/) (2022)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation](https://mlsanity.com/paper/2212.09478/) (2022)
- [HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion](https://mlsanity.com/paper/2305.06356/) (2023)
- [DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models](https://mlsanity.com/paper/2304.00916/) (2023)
- [InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://mlsanity.com/paper/2212.10550/) (2022)
- [DreamHuman: Animatable 3D Avatars from Text](https://mlsanity.com/paper/2306.09329/) (2023)
- [Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction](https://mlsanity.com/paper/2208.12697/) (2022)
- [AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control](https://mlsanity.com/paper/2303.17606/) (2023)
- [AvatarReX: Real-time Expressive Full-body Avatars](https://mlsanity.com/paper/2305.04789/) (2023)
- [DreamWaltz: Make a Scene with Complex 3D Animatable Avatars](https://mlsanity.com/paper/2305.12529/) (2023)
- [DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance](https://mlsanity.com/paper/2304.03117/) (2023)
- [StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video](https://mlsanity.com/paper/2305.00942/) (2023)

## Built on by (most cited first, 30 of 60)

- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models](https://mlsanity.com/paper/2310.08529/) (2023)
- [DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior](https://mlsanity.com/paper/2310.16818/) (2023)
- [TADA! Text to Animatable Digital Avatars](https://mlsanity.com/paper/2308.10899/) (2023)
- [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)
- [SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion](https://mlsanity.com/paper/2311.15855/) (2023)
- [Advances in 3D Generation: A Survey](https://mlsanity.com/paper/2401.17807/) (2024)
- [LLMs Meet Multimodal Generation and Editing: A Survey](https://mlsanity.com/paper/2405.19334/) (2024)
- [Disentangled Clothed Avatar Generation from Text Descriptions](https://mlsanity.com/paper/2312.05295/) (2023)
- [SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image](https://mlsanity.com/paper/2309.01855/) (2023)
- [ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems](https://mlsanity.com/paper/2312.06573/) (2023)
- [CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization](https://mlsanity.com/paper/2402.17214/) (2024)
- [Stable Score Distillation for High-Quality 3D Generation](https://mlsanity.com/paper/2312.09305/) (2023)
- [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)
- [GALA: Generating Animatable Layered Assets from a Single Scan](https://mlsanity.com/paper/2401.12979/) (2024)
- [TELA: Text to Layer-wise 3D Clothed Human Generation](https://mlsanity.com/paper/2404.16748/) (2024)
- [DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors](https://mlsanity.com/paper/2312.16837/) (2023)
- [GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and Texture Details](https://mlsanity.com/paper/2405.12420/) (2024)
- [Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation](https://mlsanity.com/paper/2407.13584/) (2024)
- [AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text](https://mlsanity.com/paper/2311.17917/) (2023)
- [Instant 3D Human Avatar Generation using Image Diffusion Models](https://mlsanity.com/paper/2406.07516/) (2024)
- [HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained Images](https://mlsanity.com/paper/2311.15672/) (2023)
- [STAR: Skeleton-aware Text-based 4D Avatar Generation with In-Network Motion Retargeting](https://mlsanity.com/paper/2406.04629/) (2024)
- [DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency](https://mlsanity.com/paper/2408.07481/) (2024)
- [DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion](https://mlsanity.com/paper/2409.17145/) (2024)
- [Text-Guided 3D Face Synthesis -- From Generation to Editing](https://mlsanity.com/paper/2312.00375/) (2023)
- [SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing](https://mlsanity.com/paper/2412.09545/) (2024)

## Abstract (arXiv)

Creating expressive, diverse and high-quality 3D avatars from highly customized text descriptions and pose guidance is a challenging task, due to the intricacy of modeling and texturing in 3D that ensure details and various styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline for generating expressive high-quality 3D avatars from nothing but text descriptions and pose guidance. In specific, we introduce a 2D diffusion model conditioned on DensePose signal to establish 3D pose control of avatars through 2D images, which enhances view consistency from partially observed scenarios. It addresses the infamous Janus Problem and significantly stablizes the generation process. Moreover, we propose a progressive high-resolution 3D synthesis strategy, which obtains substantial improvement over the quality of the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves zero-shot 3D modeling of 3D avatars that are not only more expressive, but also in higher quality and fidelity than previous works. Rigorous qualitative evaluations and user studies showcase AvatarVerse's superiority in synthesizing high-fidelity 3D avatars, leading to a new standard in high-quality and stable 3D avatar creation. Our project page is: https://avatarverse3d.github.io