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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
| Date | Stars |
|---|---|
| 2026-07-24 | 883 |
| 2026-07-25 | 883 |
| 2026-07-28 | 884 |
| 2026-07-30 | 884 |
| 2026-08-06 | 884 |
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# RingNet  This is an official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. The project was formerly referred by RingNet. The codebase consists of the inference code, i.e. give an face image using this code one can generate a 3D mesh of a complete head with the face region. For further details on the method please refer to the following publication, ``` Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision Soubhik Sanyal, Timo Bolkart, Haiwen Feng, Michael J. Black CVPR 2019 ``` More details on our NoW benchmark dataset, 3D face reconstruction challenge can be found in our [project page](https://ringnet.is.tue.mpg.de). A pdf preprint is also available on the [project page](https://ringnet.is.tue.mpg.de). * **Update**: We have changed the license agreemnt for the RingNet code and pre-trained weights. Both are now available under **MIT license** excluding the NoW Challenge dataset. * **Update**: We have released the **evaluation code for NoW Benchmark challenge** [here](https://github.com/soubhiksanyal/now_evaluation). * **Update**: Add demo to build a texture for the reconstructed mesh from the input image. * **Update**: NoW Dataset is divided into Test set and Validation Set. **Ground Truth scans** are available for the Validation Set. Please Check our [project page](https://ringnet.is.tue.mpg.de) for more details. * **Update**: We have released a **PyTorch implementation of the decoder FLAME with dynamic conture loading** which can be directly used for training networks. Please check [FLAME_PyTorch](https://github.com/soubhiksanyal/FLAME_PyTorch) for the code. ## Installation The code uses **Python 2.7** and it is tested on Tensorflow gpu version 1.12.0, with CUDA-9.0 and cuDNN-7.3. ### Setup RingNet Virtual Environment ``` virtualenv --no-site-packages <your_home_dir>/.virtualenvs/RingNet source <your_home_dir>/.virtualenvs/RingNet/bin/activate pip install --upgrade pip==19.1.1 ``` ### Clone the project and install requirements ``` git clone https://github.com/soubhiksanyal/RingNet.git cd RingNet pip install -r requirements.txt pip install opendr==0.77 mkdir model ``` Install mesh processing libraries from [MPI-IS/mesh](https://github.com/MPI-IS/mesh). (This now only works with python 3, so donot install it) * Update: Please install the following [fork](https://github.com/TimoBolkart/mesh) for working with the mesh processing libraries with python 2.7 ## Download models * Download pretrained RingNet weights from the [project website](https://ringnet.is.tue.mpg.de), downloads page. Copy this inside the **model** folder * Download FLAME 2019 model from [here](http://flame.is.tue.mpg.de/). Copy it inside the **flame_model** folder. This step is optional and only required if you want to use the output Flame parameters to play with the 3D mesh, i.e., to neutralize the pose and expression and only using the shape as a template for other methods like [VOCA (Voice Operated Character Animation)](https://github.com/TimoBolkart/voca). * Download the [FLAME_texture_data](http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip) and unpack this into the **flame_model** folder. ## Demo RingNet requires a loose crop of the face in the image. We provide two sample images in the **input_images** folder which are taken from [CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). #### Output predicted mesh rendering Run the following command from the terminal to check the predictions of RingNet ``` python -m demo --img_path ./input_images/000001.jpg --out_folder ./RingNet_output ``` Provide the image path and it will output the predictions in **./RingNet_output/images/**. #### Output predicted mesh If you want the output mesh then run the following command ``` python -m demo --img_path ./input_images/
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Max Planck Institute for Intelligent Systems
8
Sam Gao · DINQ · China
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7b80a544b946f8ed, topic:deep-learning, topic:tensorflow
matched fp:7b80a544b946f8ed, topic:computer-vision