项目介绍

TimoBolkart/FLAME-Universe 是一个托管在 GitHub 上的开源项目,目前获得 717 个 Star、34 次 Fork。

仓库全名
TimoBolkart/FLAME-Universe
Star 数
717
Fork 数
34

README 摘要

:fire: FLAME Universe :fire: This repository presents a list of publicly available ressources such as code, datasets, and scientific papers for the :fire: FLAME :fire: 3D head model. We aim at keeping the list up to date. You are invited to add missing FLAME-based ressources (publications, code repositories, datasets) either in the discussions or in a pull request. :fire: FLAME :fire: Never heard of FLAME? FLAME is…

完整内容见 GitHub 原始 README 或本页 。

常见问题

如何获取 FLAME-Universe 的源码?

可以直接克隆仓库:git clone https://github.com/TimoBolkart/FLAME-Universe.git,默认分支为 main;安装与运行步骤见仓库 README。

FLAME-Universe 最近还在维护吗?

仓库最近一次更新为 3月4日,当前没有未关闭的 Issue;Star 数为 717,可作为社区活跃度的参考。

关于

Summary of publicly available ressources such as code, datasets, and scientific papers for the FLAME 3D head model

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
825
价值分
444
活跃状态
活跃

贡献者

README

:fire: FLAME Universe :fire:

This repository presents a list of publicly available ressources such as code, datasets, and scientific papers for the :fire: FLAME :fire: 3D head model. We aim at keeping the list up to date. You are invited to add missing FLAME-based ressources (publications, code repositories, datasets) either in the discussions or in a pull request.

:fire: FLAME :fire:

Never heard of FLAME?

FLAME is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. FLAME combines a linear identity shape space (trained from head scans of 3800 subjects) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. For details please see the scientific publication.

To download the FLAME model, sign up under MPI-IS/FLAME and agree to the model license. Then you can download FLAME and other FLAME-related resources such as landmark embeddings, segmentation masks, quad template mesh, etc., from MPI-IS/FLAME/download. You can also download the model with a bash script such as fetch_FLAME.

Code

List of public repositories that use FLAME (alphabetical order).
  • BFM_to_FLAME: Conversion from Basel Face Model (BFM) to FLAME.
  • CVTHead: Controllable head avatar generation from a single image.
  • DECA: Reconstruction of 3D faces with animatable facial expression detail from a single image.
  • DiffPoseTalk: Speech-driven stylistic 3D facial animation.
  • diffusion-rig: Personalized model to edit facial expressions, head pose, and lighting in portrait images.
  • EMOCA: Reconstruction of emotional 3D faces from a single image.
  • EMOTE: Emotional speech-driven 3D face animation.
  • expgan: Face image generation with expression control.
  • FaceFormer: Speech-driven facial animation of meshes in FLAME mesh topology.
  • FateAvatar: Full-head Gaussian Avatar with Textural Editing from Monocular Video.
  • FLAME-Blender-Add-on: FLAME Blender Add-on.
  • flame-fitting: Fitting of FLAME to scans.
  • flame-head-tracker: FLAMe-based monocular video tracking.
  • FLAME_PyTorch: FLAME PyTorch layer.
  • GANHead: Animatable neural head avatar.
  • GAGAvatar: Recontruction of controllable 3D head avatars from a single image.
  • GaussianAvatars: Photorealistic head avatars with FLAME-rigged 3D Gaussians.
  • GPAvatar: Prediction of controllable 3D head avatars from one or several images.
  • GIF: Generating face images with FLAME parameter control.
  • INSTA: Volumetric head avatars from videos in less than 10 minutes.
  • INSTA-pytorch: Volumetric head avatars from videos in less than 10 minutes (PyTorch).
  • learning2listen: Modeling interactional communication in dyadic conversations.
  • LightAvatar-TensorFlow: Use of neural light field (NeLF) to build photorealistic 3D head avatars.
  • MICA: Reconstruction of metrically accurated 3D faces from a single image.
  • MeGA: Reconstruction of an editable hybrid mesh-Gaussian head avatar.
  • metrical-tracker: Metrical face tracker for monocular videos.
  • MultiTalk: Speech-driven facial animation of meshes in FLAME mesh topology.
  • NED: Facial expression of emotion manipulation in videos.
  • Next3D: 3D generative model with FLAME parameter control.
  • NeuralHaircut: Creation of strand-based hairstyle from single-view or multi-view videos.
  • neural-head-avatars: Building a neural head avatar from video sequences.
  • NeRSemble: Building a neural head avatar from multi-view video data.
  • photometric_optimization: Fitting of FLAME to images using differentiable rendering.-
  • RGBAvatar: Reduced Gaussian Blendshapes for Online Modeling of Head Avatars.
  • RingNet: Reconstruction of 3D faces from a single image.
  • ROME: Creation of personalized avatar from a single image.
  • SAFA: Animation of face images.
  • Semantify: Semantic control over 3DMM parameters.
  • SPARK: Personalized Real-time Monocular Face Capture
  • SPECTRE: Speech-aware 3D face reconstruction from images.
  • SplattingAvatar: Real-time human avatars with mesh-embedded Gaussian splatting.
  • TalkingStyle: Personalized spech-driven 3D facial animation of meshes in FLAME mesh topology.
  • SMIRK: Reconstruction of emotional 3D faces from a single image.
  • TRUST: Racially unbiased skin tone extimation from images.
  • TF_FLAME: Fit FLAME to 2D/3D landmarks, FLAME meshes, or sample textured meshes.
  • video-head-tracker: Track 3D heads in video sequences.
  • VOCA: Speech-driven facial animation of meshes in FLAME mesh topology.
  • VHAP: 3D face tracker for single-view and multi-view videos.

Datasets

List of datasets with meshes in FLAME topology.
  • BP4D+: 127 subjects, one neutral expression mesh each.
  • CoMA dataset: 12 subjects, 12 extreme dynamic expressions each.
  • D3DFACS: 10 subjects, 519 dynamic expressions in total.
  • Decaf dataset: Deformation capture for face and hand interactions.
  • FaceWarehouse: 150 subjects, one neutral expression mesh each.
  • FaMoS: 95 subjects, 28 dynamic expressions and head poses each, about 600K frames in total.
  • Florence 2D/3D: 53 subjects, one neutral expression mesh each.
  • FRGC: 531 subjects, one neutral expression mesh each.
  • LYHM: 1216 subjects, one neutral expression mesh each.
  • MEAD reconstructions: 3D face reconstructions for MEAD (emotional talking-face dataset).
  • MEAD-3D: FLAME meshes and model parameters for MEAD.
  • NeRSemble dataset: 10 sequences of multi-view images and 3D faces in FLAME mesh topology.
  • RenderMe-360: Digital asset library for high-fidelity head avatars with labeled FLAME parameters.
  • SingingHead: 27 hours of synchronized singing video, audio, 3D facial motion, and background music from 76 subjects.
  • Stirling: 133 subjects, one neutral expression mesh each.
  • VOCASET: 12 subjects, 40 speech sequences each with synchronized audio.

Publications

List of FLAME-based scientific publications.
2026
2025
2024
2023
2022
2021
2020
2019

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