Metadata-Version: 2.1
Name: pix2vertex
Version: 1.0.2
Summary: 3D face reconstruction from a single image
Home-page: https://github.com/eladrich/pix2vertex.pytorch
Author: Elad Richardson, Matan Sela
Author-email: elad.richardson@gmail.com, matansel@gmail.com
License: UNKNOWN
Description: # Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation - Official Pytorch Implementation
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/eladrich/pix2vertex.pytorch/mybinder-test?filepath=reconstruct_pipeline.ipynb)
        
        
        [[Arxiv]](https://arxiv.org/pdf/1703.10131.pdf) [[Video]](https://www.youtube.com/watch?v=6lUdSVcBB-k)
        
        
        Evaluation code for Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation. Finally ported to pyTorch!
        
        <img src="examples/jupyter_gif.gif" width="900px"/>
        
        
        ## Recent Updates
        
        **`2020.05.06`**: Added [myBinder](https://mybinder.org/v2/gh/eladrich/pix2vertex.pytorch/mybinder-test?filepath=reconstruct_pipeline.ipynb) version for quick testing of the model
        
        **`2020.04.30`**: Initial pyTorch release
        
        # What's in this release?
        
        The [original pix2vertex repo](https://github.com/matansel/pix2vertex) was composed of three parts
         - A `lua` based network to perform the image to depth + correspondence maps trained on synthetic facial data
         - A `Matlab` based non-rigid ICP scheme for converting the output maps to a full 3D Mesh  
         - A `Matlab` based shape-from-shading scheme for adding fine mesoscopic details
         
         This repo currently contains our image-to-image network with weights and model ported from the `lua` version and a simple `python` postprocessing scheme.
         - The released network was trained on a combination of synthetic images and unlabeled real images for some extra robustness :)
        
        ## Setup
        ```
            $ git clone https://github.com/matansel/pix2vertex.pytorch.git
            $ cd pix2vertex.pytorch
            $ python setup.py install
        ```
        ## Usage
        ```
         import pix2vertex as p2v
         from imageio import imread
         image = imread(<some image file>)
         
         depth, cropped = p2v.reconstruct(image)
         
         p2v.vis_depth_interactive(depth)
        ```
        
        ### Pretrained Model
        Models can be downloaded from these links:
        - [pix2vertex model](https://drive.google.com/open?id=1op5_zyH4CWm_JFDdCUPZM4X-A045ETex)
        - [dlib landmark predictor](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2), note that the dlib model has its own license.
        
        or simply by running `download.sh`
        
        
        ## TODOs
        - [x] Port Torch model to pyTorch
        - [x] Release an inference notebook (using [K3D](https://github.com/K3D-tools/K3D-jupyter))
        - [x] Add requirements
        - [x] Pack as wheel?
        - [x] Ported to MyBinder
        - [ ] Port the Shape-from-Shading method used in our matlab paper
        - [ ] Write a short blog about the revised training scheme 
        
        ## Citation
        If you use this code for your research, please cite our paper <a href="https://arxiv.org/pdf/1703.10131.pdf">Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation</a>:
        
        ```
        @article{sela2017unrestricted,
          title={Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation},
          author={Sela, Matan and Richardson, Elad and Kimmel, Ron},
          journal={arxiv},
          year={2017}
        }
        ```
        
Keywords: pix2vertex face reconstruction 3d pytorch pip package
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
