[CVPR'23] Learning Neural Parametric Head Models
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Updated
Jun 3, 2024 - Python
[CVPR'23] Learning Neural Parametric Head Models
Official Pytorch Implementation of SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis (CVPR 2024)
Summary of publicly available ressources such as code, datasets, and scientific papers for the FLAME 3D head model
Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild
This codebase demonstrates how to synthesize realistic 3D character animations given an arbitrary speech signal and a static character mesh.
Blender Add-on for the FLAME face model
A high-fidelity 3D face reconstruction library from monocular RGB image(s)
This is a implementation of the 3D FLAME model in PyTorch
Visualize 3d meshes via scripting in scheme
Tensorflow framework for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 2D or 3D keypoints, and how to generate textured head meshes from Images.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
Example code for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 3D keypoints and 3D scans.
The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.
Blender add-on to implement VOCA neural network.
Regional 3D face reconstruction (Ocular Region)
The provided program jointly optimizes a multilinear face model and the registration of the face scans used for model training.
The provided program loads a multilinear face model and fits this model to a point cloud or a triangle mesh.
The program loads a global or a local linear statistical face model and fits it to a point cloud or triangle mesh.
The provided program loads a multilinear wavelet model and fits this model to a point cloud or a triangle mesh.
The provided program robustly learns a multilinear face model from databases with missing data, corrupt data, wrong semantic correspondence, and inaccurate vertex correspondence.
Add a description, image, and links to the morphable-model topic page so that developers can more easily learn about it.
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