{"id":14669546,"url":"https://github.com/pansanity666/Awesome-Avatars","last_synced_at":"2025-09-08T23:31:33.330Z","repository":{"id":195712612,"uuid":"693491234","full_name":"pansanity666/Awesome-Avatars","owner":"pansanity666","description":"List of recent advances for human avatars, including generation, reconstruction, and editing, 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Resources","Other Lists"],"sub_categories":["Survey and Awesome Repos","TeX Lists"],"readme":"# Awesome Avatars [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nList of recent advances for human avatars, including generation, reconstruction, and editing, etc.\n\nIf you find any missed paper, feel free to open an issue or PR.\n\n## Table of Contents \u003c!-- omit in toc --\u003e\n\n\u003c!-- - [Open-source Toolboxes and Foundation Models](#open-source-toolboxes-and-foundation-models) --\u003e\n\n- [Awesome Avatars ](#awesome-avatars-)\n  - [Avatar Generation](#avatar-generation)\n  - [Per-subject Avatar Reconstruction](#per-subject-avatar-reconstruction)\n  - [Generalizable Avatar Novel View Synthesis](#generalizable-avatar-novel-view-synthesis)\n  - [Generalizable Avatar Mesh Reconstruction](#generalizable-avatar-mesh-reconstruction)\n  - [Text-to-Avatar](#text-to-avatar)\n  - [Avatar Interaction](#avatar-interaction)\n  - [Motion Generation](#motion-generation)\n  - [SMPL Estimation](#smpl-estimation)\n  - [Dataset](#dataset)\n  - [Aknowledgement](#aknowledgement)\n\n### Avatar Generation\n\n- [Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations](https://arxiv.org/pdf/2204.08839.pdf) (ECCV 2022)  \n  [![Star](https://img.shields.io/github/stars/nogu-atsu/ENARF-GAN?style=social)](https://github.com/nogu-atsu/ENARF-GAN)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2204.08839.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://github.com/nogu-atsu/ENARF-GAN)\n\n- [GENERATIVE NEURAL ARTICULATED RADIANCE FIELDS](https://arxiv.org/pdf/2206.14314.pdf) (NeurIPS 2022)  \n  [![Star](https://img.shields.io/github/stars/alexanderbergman7/GNARF?style=social)](https://github.com/alexanderbergman7/GNARF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2206.14314.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://github.com/alexanderbergman7/GNARF)\n\n- [AvatarGen: a 3D Generative Model for Animatable Human Avatars](http://arxiv.org/abs/2208.00561) (arXiv 01/08/2022)  \n  [![Star](https://img.shields.io/github/stars/jfzhang95/AvatarGen?style=social)](https://github.com/jfzhang95/AvatarGen)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2208.00561)\n\n- [EVA3D: Compositional 3D Human Generation from 2D Image Collections](https://arxiv.org/pdf/2210.04888.pdf) (ICLR 2023 Spotlight)  \n  [![Star](https://img.shields.io/github/stars/hongfz16/EVA3D?style=social)](https://github.com/hongfz16/EVA3D)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2210.04888.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://hongfz16.github.io/projects/EVA3D)\n\n- [AG3D: Learning to Generate 3D Avatars from 2D Image Collections](https://arxiv.org/pdf/2305.02312.pdf) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/zj-dong/AG3D?style=social)](https://github.com/zj-dong/AG3D)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2305.02312.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zj-dong.github.io/AG3D)\n\n- [Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors.](https://arxiv.org/abs/2302.01162) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/X-zhangyang/Get3DHuman?style=social)](https://github.com/X-zhangyang/Get3DHuman/)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2302.01162)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://x-zhangyang.github.io/2023_Get3DHuman/)\n\n- [3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective.](https://arxiv.org/abs/2204.13096) (arXiv 2022)  \n  [![Star](https://img.shields.io/github/stars/layumi/3D-Magic-Mirror?style=social)](https://github.com/layumi/3D-Magic-Mirror)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2204.13096)\n\n### Per-subject Avatar Reconstruction\n\n- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://arxiv.org/pdf/2012.15838.pdf) (CVPR 2021)  \n  [![Star](https://img.shields.io/github/stars/zju3dv/neuralbody?style=social)](https://github.com/zju3dv/neuralbody)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2012.15838.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zju3dv.github.io/neuralbody/)\n\n- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://arxiv.org/pdf/2212.07422.pdf) (ICCV 2021)  \n  [![Star](https://img.shields.io/github/stars/zju3dv/animatable_nerf?style=social)](https://github.com/zju3dv/animatable_nerf)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2212.07422.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zju3dv.github.io/animatable_nerf/)\n\n- [Neural Human Radiance Field from a Single Video](https://arxiv.org/pdf/2203.12575.pdf) (ECCV 2022)  \n  [![Star](https://img.shields.io/github/stars/apple/ml-neuman?style=social)](https://github.com/apple/ml-neuman)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2203.12575.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://github.com/apple/ml-neuman)\n\n- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://arxiv.org/abs/2201.04127) (CVPR 2022)  \n  [![Star](https://img.shields.io/github/stars/chungyiweng/humannerf?style=social)](https://github.com/chungyiweng/humannerf)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2201.04127)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://grail.cs.washington.edu/projects/humannerf/)\n\n- [MonoHuman: Animatable Human Neural Field from Monocular Video](https://arxiv.org/abs/2304.02001) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/Yzmblog/MonoHuman?style=social)](https://github.com/Yzmblog/MonoHuman)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2304.02001)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://yzmblog.github.io/projects/MonoHuman/)\n\n- [InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://arxiv.org/pdf/2012.15838.pdf) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/tijiang13/InstantAvatar?style=social)](https://github.com/tijiang13/InstantAvatar)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2212.07422.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://tijiang13.github.io/InstantAvatar/)\n\n- [Vid2Avatar: 3D Avatar Reconstruction From Videos in the Wild via Self-Supervised Scene Decomposition](https://arxiv.org/abs/2302.11566) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/MoyGcc/vid2avatar?style=social)](https://github.com/MoyGcc/vid2avatar)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2302.11566)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://moygcc.github.io/vid2avatar/)\n\n- [Relightable and Animatable Neural Avatar from Sparse-View Video](http://arxiv.org/abs/2308.07903) (arXiv 17/08/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2308.07903)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zju3dv.github.io/relightable_avatar/)\n\n- [TeCH: Text-guided Reconstruction of Lifelike Clothed Humans](http://arxiv.org/abs/2308.08545) (3DV 2024)  \n  [![Star](https://img.shields.io/github/stars/huangyangyi/TeCH?style=social)](https://github.com/huangyangyi/TeCH)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2308.08545)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://huangyangyi.github.io/TeCH/)\n\n- [Learning Neural Volumetric Representations of Dynamic Humans in Minutes](http://arxiv.org/abs/2302.12237) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/zju3dv/instant-nvr?style=social)](https://github.com/zju3dv/instant-nvr)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2302.12237)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zju3dv.github.io/instant_nvr)\n\n- [HUGS: Human Gaussian Splats](http://arxiv.org/abs/2311.17910) (Arxiv 2023)  \n  [![Star](https://img.shields.io/github/stars/apple/ml-hugs?style=social)](https://github.com/apple/ml-hugs)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2311.17910)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://machinelearning.apple.com/research/hugs)\n\n- [GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians](http://arxiv.org/abs/2312.02134) (Arxiv 2023)  \n  [![Star](https://img.shields.io/github/stars/huliangxiao/GaussianAvatar?style=social)](https://github.com/huliangxiao/GaussianAvatar)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2312.02134)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://huliangxiao.github.io/GaussianAvatar)\n\n- [GART: Gaussian Articulated Template Models](https://arxiv.org/abs/2311.16099) (Arxiv 2023)  \n  [![Star](https://img.shields.io/github/stars/JiahuiLei/GART?style=social)](https://github.com/JiahuiLei/GART)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2311.16099)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://www.cis.upenn.edu/~leijh/projects/gart/)\n\n- [Human101: Training 100+FPS Human Gaussians in 100s from 1 View](https://arxiv.org/abs/2312.15258) (Arxiv 2023)  \n  [![Star](https://img.shields.io/github/stars/longxiang-ai/Human101?style=social)](https://github.com/longxiang-ai/Human101)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2312.15258)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://longxiang-ai.github.io/Human101/)\n\n### Generalizable Avatar Novel View Synthesis\n\n- [Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering](https://arxiv.org/pdf/2109.07448.pdf) (NeurIPS 2021)  \n  [![Star](https://img.shields.io/github/stars/YoungJoongUNC/Neural_Human_Performer?style=social)](https://github.com/YoungJoongUNC/Neural_Human_Performer)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2109.07448.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://youngjoongunc.github.io/nhp/)\n\n- [MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images](https://arxiv.org/abs/2203.16875) (TPAMI 2022)  \n  [![Star](https://img.shields.io/github/stars/gaoxiangjun/MPS-NeRF?style=social)](https://github.com/gaoxiangjun/MPS-NeRF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2203.16875)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://gaoxiangjun.github.io/mps_nerf/)\n\n- [GP-NeRF: Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering](https://arxiv.org/pdf/2112.04312.pdf) (ECCV 2022)  \n  [![Star](https://img.shields.io/github/stars/sail-sg/GP-Nerf?style=social)](https://github.com/sail-sg/GP-Nerf)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2112.04312.pdf)\n\n- [HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs](https://arxiv.org/pdf/2112.02789.pdf) (CVPR 2022)  \n  [![Star](https://img.shields.io/github/stars/zhaofuq/HumanNeRF?style=social)](https://github.com/zhaofuq/HumanNeRF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2112.02789.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zhaofuq.github.io/humannerf/)\n\n- [MonoNHR: Monocular Neural Human Renderer](https://arxiv.org/abs/2210.00627) (3DV 2022)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2210.00627)\n\n- [Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors](https://arxiv.org/abs/2208.11905) (TVCG 2023)  \n  [![Star](https://img.shields.io/github/stars/Talegqz/neural_novel_actor?style=social)](https://github.com/Talegqz/neural_novel_actor)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2208.11905)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://talegqz.github.io/neural_novel_actor/)\n\n- [GHuNeRF: Generalizable Human NeRF from a Monocular Video](https://arxiv.org/pdf/2308.16576v2.pdf) (arXiv 03/09/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2308.16576v2.pdf)\n\n- [Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling](https://arxiv.org/pdf/2304.04897.pdf) (ICLR 2023)\n\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2304.04897.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://youngjoongunc.github.io/nia/)\n\n- [ActorsNeRF: Animatable Few-shot Human Rendering with Generalizable NeRFs](https://arxiv.org/abs/2304.14401) (ICCV 2023)\n\n  [![Star](https://img.shields.io/github/stars/JitengMu/ActorsNeRF?style=social)](https://github.com/JitengMu/ActorsNeRF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2304.14401)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://jitengmu.github.io/ActorsNeRF/)\n\n- [SHERF: Generalizable Human NeRF from a Single Image](https://arxiv.org/abs/2303.12791) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/skhu101/SHERF?style=social)](https://github.com/skhu101/SHERF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2303.12791)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://skhu101.github.io/SHERF/)\n\n- [TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering](https://arxiv.org/abs/2307.12291) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/pansanity666/TransHuman?style=social)](https://github.com/pansanity666/TransHuman/)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2307.12291)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://pansanity666.github.io/TransHuman/)\n\n- [GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis](http://arxiv.org/abs/2312.02155) (Arxiv)  \n  [![Star](https://img.shields.io/github/stars/ShunyuanZheng/GPS-Gaussian?style=social)](https://github.com/ShunyuanZheng/GPS-Gaussian)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2312.02155)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://shunyuanzheng.github.io/GPS-Gaussian)\n\n### Generalizable Avatar Mesh Reconstruction\n\n- [ICON : Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://arxiv.org/pdf/2112.09127.pdf) (CVPR 2022)  \n  [![Star](https://img.shields.io/github/stars/YuliangXiu/ICON?style=social)](https://github.com/YuliangXiu/ICON)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2112.09127.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://icon.is.tue.mpg.de/)\n\n- [ECON: Explicit Clothed humans Optimized via Normal integration](https://arxiv.org/pdf/2212.07422.pdf) (CVPR 2023 Highlight)  \n  [![Star](https://img.shields.io/github/stars/YuliangXiu/ECON?style=social)](https://github.com/YuliangXiu/ECON)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2212.07422.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://xiuyuliang.cn/econ/)\n\n- [Structured 3D Features for Reconstructing Relightable and Animatable Avatars](https://arxiv.org/abs/2212.06820) (CVPR 2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2212.06820)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://enriccorona.github.io/s3f/)\n\n- [SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction](https://yukangcao.github.io/SeSDF/index_files/SeSDF_05448.pdf) (CVPR 2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2304.00359)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://yukangcao.github.io/SeSDF/)\n\n- [DIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction](https://openaccess.thecvf.com/content/CVPR2023/papers/Song_DIFu_Depth-Guided_Implicit_Function_for_Clothed_Human_Reconstruction_CVPR_2023_paper.pdf) (CVPR 2023)  \n  [![Website](https://img.shields.io/badge/Website-9cf)](https://eadcat.github.io/DIFu/)\n\n- [Complete 3D Human Reconstruction from a Single Incomplete Image](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.pdf) (CVPR 2023)  \n  [![Website](https://img.shields.io/badge/Website-9cf)](https://junyingw.github.io/paper/3d_inpainting/)\n\n- [High-fidelity 3D Human Digitization from Single 2K Resolution Images](https://arxiv.org/pdf/2303.15108.pdf) (CVPR 2023 Highlight)  \n  [![Star](https://img.shields.io/github/stars/SangHunHan92/2K2K?style=social)](https://github.com/SangHunHan92/2K2K)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2303.15108.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://sanghunhan92.github.io/conference/2K2K/)\n\n- [D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field](https://arxiv.org/pdf/2308.08857.pdf) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/psyai-net/D-IF_release?style=social)](https://github.com/psyai-net/D-IF_release)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2308.08857.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://yxt7979.github.io/idf/)\n\n- [Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction](https://arxiv.org/pdf/2112.09127.pdf) (NeurIPS 2023)  \n  [![Star](https://img.shields.io/github/stars/River-Zhang/GTA?style=social)](https://github.com/River-Zhang/GTA)\n\n### Text-to-Avatar\n\n- [ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image.](https://arxiv.org/abs/2305.16411) (arXiv 25/5/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2305.16411)\n\n- [DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models](http://arxiv.org/abs/2304.00916) (arXiv 06/04/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2304.00916)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://yukangcao.github.io/DreamAvatar/)\n\n- [DreamHuman: Animatable 3D Avatars from Text](https://arxiv.org/abs/2306.09329) (arXiv 15/06/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2306.09329)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://dream-human.github.io/)\n\n- [AvatarVerse: High-quality \u0026 Stable 3D Avatar Creation from Text and Pose](http://arxiv.org/abs/2308.03610) (arXiv 07/08/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2308.03610)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://avatarverse3d.github.io/)\n\n- [Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with Image Diffusion Model](https://arxiv.org/abs/2308.07749) (arXiv 15/08/2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2308.07749)\n\n- [DreamWaltz: Make a Scene with Complex 3D Animatable Avatars](https://arxiv.org/pdf/2305.12529) (arXiv 12/07/2023)  \n  [![Star](https://img.shields.io/github/stars/IDEA-Research/DreamWaltz?style=social)](https://github.com/IDEA-Research/DreamWaltz)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2305.12529)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://idea-research.github.io/DreamWaltz/)\n\n- [AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control](https://arxiv.org/pdf/2303.17606) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/songrise/AvatarCraft?style=social)](https://github.com/songrise/AvatarCraft)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2303.17606)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://avatar-craft.github.io)\n\n- [AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars](https://arxiv.org/pdf/2205.08535) (SIGGRAPH 2022)  \n  [![Star](https://img.shields.io/github/stars/hongfz16/AvatarCLIP?style=social)](https://github.com/hongfz16/AvatarCLIP)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2205.08535)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://hongfz16.github.io/projects/AvatarCLIP.html)\n\n- [AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation](https://arxiv.org/abs/2306.09864) (arXiv 16/06/2023)  \n  [![Star](https://img.shields.io/github/stars/zeng-yifei/AvatarBooth?style=social)](https://github.com/zeng-yifei/AvatarBooth)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2306.09864)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://zeng-yifei.github.io/avatarbooth_page/)\n\n- [AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion](https://arxiv.org/pdf/2307.06526.pdf) (ACMMM 2023)  \n  [![Star](https://img.shields.io/github/stars/HansenHuang0823/AvatarFusion?style=social)](https://github.com/HansenHuang0823/AvatarFusion)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2307.06526.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://hansenhuang0823.github.io/AvatarFusion/)\n\n- [TADA! Text to Animatable Digital Avatars](https://arxiv.org/abs/2308.10899) (arXiv 2023)  \n  [![Star](https://img.shields.io/github/stars/TingtingLiao/TADA?style=social)](https://github.com/TingtingLiao/TADA)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2308.10899)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://tada.is.tue.mpg.de/)\n\n- [Guide3D: Create 3D Avatars from Text and Image Guidance](https://arxiv.org/pdf/2308.09705.pdf) (arXiv 2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2308.09705.pdf)\n\n- [HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation](https://arxiv.org/abs/2310.01406) (arXiv 2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2310.01406)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://humannorm.github.io/)\n\n- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](http://arxiv.org/abs/2311.17117) (Arxiv)  \n  [![Star](https://img.shields.io/github/stars/HumanAIGC/AnimateAnyone?style=social)](https://github.com/HumanAIGC/AnimateAnyone)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2311.17117)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://humanaigc.github.io/animate-anyone/)\n\n- [HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting](http://arxiv.org/abs/2311.17061) (Arxiv)  \n  [![Star](https://img.shields.io/github/stars/alvinliu0/HumanGaussian?style=social)](https://github.com/alvinliu0/HumanGaussian)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](http://arxiv.org/abs/2311.17061)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://alvinliu0.github.io/projects/HumanGaussian)\n\n- [InstructHumans: Editing Animatable 3D Human Textures with Instructions](https://arxiv.org/abs/2404.04037) (Arxiv 2024)  \n  [![Star](https://img.shields.io/github/stars/viridityzhu/InstructHumans?style=social)](https://github.com/viridityzhu/InstructHumans)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2404.04037)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://jyzhu.top/instruct-humans/)\n\n### Avatar Interaction\n\n- [Hi4D: 4D Instance Segmentation of Close Human Interaction](https://arxiv.org/abs/2303.15380v1) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/yifeiyin04/Hi4D?style=social)](https://github.com/yifeiyin04/Hi4D)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2303.15380v1)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://yifeiyin04.github.io/Hi4D/)\n\n- [NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions.](https://arxiv.org/abs/2212.07626) (CVPR 2023)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2212.07626)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://juzezhang.github.io/NeuralDome/)\n\n- [HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video](https://arxiv.org/abs/2304.12281) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/TencentARC/HOSNeRF?style=social)](https://github.com/TencentARC/HOSNeRF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2304.12281)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://showlab.github.io/HOSNeRF/)\n\n### Motion Generation\n\n- [MotionGPT: Human Motion as a Foreign Language](https://arxiv.org/pdf/2306.14795.pdf) (arxiv)  \n  [![Star](https://img.shields.io/github/stars/OpenMotionLab/MotionGPT?style=social)](https://github.com/OpenMotionLab/MotionGPT)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2306.14795.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://motion-gpt.github.io)\n\n- [Executing your Commands via Motion Diffusion in Latent Space](https://arxiv.org/pdf/2212.04048.pdf) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/ChenFengYe/motion-latent-diffusion?style=social)](https://github.com/ChenFengYe/motion-latent-diffusion)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2212.04048.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://chenxin.tech/mld/)\n\n- [TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis](https://arxiv.org/pdf/2305.00976.pdf) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/Mathux/TMR?style=social)](https://github.com/Mathux/TMR)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2305.00976.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://mathis.petrovich.fr/tmr/)\n\n- [Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation](https://arxiv.org/pdf/2305.00976.pdf) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/Mathux/TMR?style=social)](https://github.com/Mathux/TMR)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2305.00976.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://mathis.petrovich.fr/tmr/)\n\n### SMPL Estimation\n\n- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://arxiv.org/abs/1909.12828) (ICCV 2019)  \n  [![Star](https://img.shields.io/github/stars/nkolot/SPIN?style=social)](https://github.com/nkolot/SPIN)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/1909.12828)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://www.nikoskolot.com/projects/spin/)\n\n- [End-to-end human pose and mesh reconstruction with transformers.](https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper.pdf) (CVPR 2021)  \n  [![Star](https://img.shields.io/github/stars/microsoft/MeshTransformer?style=social)](https://github.com/microsoft/MeshTransformer)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2012.09760.pdf)\n\n- [Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_HybrIK_A_Hybrid_Analytical-Neural_Inverse_Kinematics_Solution_for_3D_Human_CVPR_2021_paper.pdf) (CVPR 2021)  \n  [![Star](https://img.shields.io/github/stars/Jeff-sjtu/HybrIK?style=social)](https://github.com/Jeff-sjtu/HybrIK)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2011.14672.pdf)\n\n- [GLAMR: Global occlusion-aware human mesh recovery with dynamic cameras](https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_GLAMR_Global_Occlusion-Aware_Human_Mesh_Recovery_With_Dynamic_Cameras_CVPR_2022_paper.pdf) (CVPR 2022 Oral)  \n  [![Star](https://img.shields.io/github/stars/NVlabs/GLAMR?style=social)](https://github.com/NVlabs/GLAMR)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2112.01524.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://nvlabs.github.io/GLAMR/)\n\n- [D\u0026d: Learning human dynamics from dynamic camera](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136650470.pdf) (ECCV 2022 Oral)  \n  [![Star](https://img.shields.io/github/stars/Jeff-sjtu/DnD?style=social)](https://github.com/Jeff-sjtu/DnD)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2209.08790.pdf)\n\n- [CLIFF: Carrying location information in full frames into human pose and shape estimation](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136650580.pdf) (ECCV 2022 Oral)  \n  [![Star](https://img.shields.io/github/stars/haofanwang/CLIFF?style=social)](https://github.com/haofanwang/CLIFF)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2208.00571.pdf)\n\n- [Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation](https://arxiv.org/pdf/2303.14747.pdf) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/sxl142/GLoT?style=social)](https://github.com/sxl142/GLoT)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2303.14747.pdf)\n\n- [JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery](https://arxiv.org/abs/2307.16377) (ICCV 2023)  \n  [![Star](https://img.shields.io/github/stars/xljh0520/jotr?style=social)](https://github.com/xljh0520/jotr)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2307.16377)\n\n- [TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments](https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_TRACE_5D_Temporal_Regression_of_Avatars_With_Dynamic_Cameras_in_CVPR_2023_paper.pdf) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/Arthur151/ROMP?style=social)](https://github.com/Arthur151/ROMP)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2306.02850.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://www.yusun.work/TRACE/TRACE.html)\n\n### Dataset\n\n- [Function4D: Real-time Human Volumetric Capture from Very Sparse RGBD Sensors (Thuman-2.0 Dataset)](https://arxiv.org/abs/2105.01859) (CVPR 2021 Oral)  \n  [![Star](https://img.shields.io/github/stars/ytrock/THuman2.0-Dataset?style=social)](https://github.com/ytrock/THuman2.0-Dataset)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2105.01859)\n  [![Website](https://img.shields.io/badge/Website-9cf)](http://www.liuyebin.com/Function4D/Function4D.html)\n\n- [HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling](https://arxiv.org/pdf/2204.13686.pdf) (ECCV 2022 Oral)  \n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/pdf/2204.13686.pdf)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://caizhongang.github.io/projects/HuMMan/)\n\n- [CLOTH4D: A Dataset for Clothed Human Reconstruction](https://openaccess.thecvf.com/content/CVPR2023/papers/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.pdf) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/AemikaChow/CLOTH4D?style=social)](https://github.com/AemikaChow/CLOTH4D)\n\n- [High-fidelity 3D Human Digitization from Single 2K Resolution Images (2K2K)](https://arxiv.org/abs/2303.15108) (CVPR 2023)  \n  [![Star](https://img.shields.io/github/stars/SangHunHan92/2K2K?style=social)](https://github.com/SangHunHan92/2K2K)\n  [![arXiv](https://img.shields.io/badge/arXiv-b31b1b.svg)](https://arxiv.org/abs/2303.15108)\n  [![Website](https://img.shields.io/badge/Website-9cf)](https://sanghunhan92.github.io/conference/2K2K/)\n\n### Aknowledgement\n\n- We thank the template from [Awesome-Video-Diffusion](https://github.com/showlab/Awesome-Video-Diffusion).\n\n- The main contributors of this project are from ReLER Lab, CCAI, Zhejiang University.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpansanity666%2FAwesome-Avatars","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpansanity666%2FAwesome-Avatars","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpansanity666%2FAwesome-Avatars/lists"}