Code for 3D aware implicit Generative Adversarial Network. Please mind the remarks if you intend to make use of this code.
This repository extends a lightweight generative network to learn a distribution of 2D image UV textures wrapped on an underlying geometry, from a dataset of single-view photographs. Given a mesh prior, the generator synthesises UV appearance textures which are then rendered on top of the geometry. Colored points are sampled from the mesh and displaced along the mesh normal according to the last UV texture channel, which operates as a displacement map.
As stated above, this code builds on top of an implementation by GitHub user lucidrains. The mentioned code license is provided in the below toggle.
Lightweight GAN license
MIT License
Copyright (c) 2021 Phil Wang
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SOFTWARE.Clone this repository and install the dependencies with the below commands.
git clone https://github.com/maximeraafat/3DiGAN.git
pip install -r 3DiGAN/requirements.txtThe point-based rendering framework utilises PyTorch3D. Checkout the steps described in their provided installation instruction set with matching versions of PyTorch, and CUDA if applicable.
Learning a human appearance model requires an underlying geometry prior : 3DiGAN leverages the SMPLX parametric body model. Download the body model SMPLX_NEUTRAL.npz and corresponding UVs smplx_uv.obj from the SMPLX project page into a shared folder. For training, we require a large collection of single view full body human images and their respective per image body parameters. Instead of storing the SMPLX underlying meshes individually, we store the body parameters for the full dataset into an npz file. Our code requires a specific structure for SMPLX, which we extract from the estimated body parameters with PIXIE.
Details on how to get SMPLX parameters for 3DiGAN with PIXIE
Our code expects a npz file containing a list of 8 tensors : ['global_orient', 'body_pose', 'jaw_pose', 'left_hand_pose', 'right_hand_pose', 'expression', 'betas', 'cam']. All per subject parameters are obtained from the PIXIE output in the following way.
import numpy as np
params = np.load(<name>_param.pkl, allow_pickle=True)
prediction = np.load(<name>_prediction.pkl, allow_pickle=True)
global_orient = params['global_pose']
body_pose = params['body_pose']
jaw_pose = params['jaw_pose']
left_hand_pose = params['left_hand_pose']
right_hand_pose = params['right_hand_pose']
expression = params['exp'][:10]
betas = params['shape'][:10]
cam = prediction['cam']<name>_param.pkl and <name>_prediction.pkl are the respective PIXIE outputs for a given image. Finally, the SMPLX parameters are concatenated together for all subjects in the training dataset of interest. For instance, the final global orientation shape will be global_orient.shape = (num_subjects, 1, 3, 3), where the equivalent shape for one single SMPLX body is (1, 3, 3). An example of SMPLX parameters extracted with PIXIE for version 1.0 of the SHHQ dataset, containing 40'000 images of high-quality full-body humans, is accessible here.
Our code's purpose is the learning and synthesis of novel appearances; here we provide instructions for two different scenarios.
Given a large dataset of full body humans (see SHHQ) and corresponding SMPLX parameters, execute the following command.
python 3DiGAN/main.py --data <path/to/dataset> \
--models_dir <path/to/output/models> \
--results_dir <path/to/output/results> \
--name <run/name> \
--render \
--smplx_model_path <path/to/smplx>The --smplx_model_path option provides the path to the SMPLX models folder, and requires an npz file containing all the estimated SMPLX parameters for each image in the dataset. See the installations section for details. The npz file must be accessible either by
- renaming it to
dataset.npzand including the file to the dataset folder under<path/to/dataset>, or by - providing the path to the npz file with
--labelpath <path/to/npz>
To synthesise appearance for an arbitrary fixed geometry prior, provide the path to an obj mesh file containing UVs with --mesh_obj_path.
python 3DiGAN/main.py --data <path/to/dataset> \
--models_dir <path/to/output/models> \
--results_dir <path/to/output/results> \
--name <run/name> \
--render \
--mesh_obj_path <path/to/obj>The --mesh_obj_path option requires a json file contaning estimated or ground truth camera azimuth and elevations for each image in the dataset. Note that the focal length to our point rendering camera is fixed to 10. Analogously to the human apperance modelling section, the json file must be accessible either by
- renaming it to
dataset.jsonand including the file to the dataset folder under<path/to/dataset>, or by - providing the path to the json file with
--labelpath <path/to/json>
A toy dataset containing 2'000 renders of the PyTorch3D cow mesh with corresponding json file comprising camera pose labels is accessible here. The cow obj mesh file is accessible under this link.
To synthesise new human appearances from a trained generator, execute this command.
python 3DiGAN/main.py --generate \
--models_dir <path/to/output/models> \
--results_dir <path/to/output/results> \
--name <run/name> \
--render \
--labelpath <path/to/npz> \
--smplx_model_path <path/to/smplx>Unlike for training, generation requires the --labelpath option since the dataset path is not provided. To synthesise arbitrary geometry appearances, replace the --smplx_model_path option for --mesh_obj_path and adapt --labelpath.
This section discusses the relevant command line arguments. The code follows a similar structure to the original lightweight GAN implementation and supports the same options, while adding arguments for the rendering environment. Please visit the parent repository for further details.
--render_size: square rendering resolution, by default set to256. This flag does not replace--image_size(also by default set to256), which is the generated square UV map resolution--render: whether to render the learned generated output. Without this flag, the code is essentially a copy of lightweight GAN--renderer: set by default todefault, defines which point renderer to use. Has to be one ofdefaultorpulsar--nodisplace: call this flag to learn RGB appearances only, without a fourth displacement channel--num_points: number of points sampled from the underlying mesh geometry, by default set to10**5--gamma: point transparency coefficient for pulsar (defined between 1e-5 and 1), by default set to1e-3--radius: point radius, set by default to0.01for the default renderer and to0.0005for pulsar--smplx_model_path: path to the SMPLX models folder--mesh_obj_path: path to the underlying obj mesh file--labelpath: path to the npz file, respectively json file, containing the necessary SMPLX parameters or camera poses for rendering
Note that the generated UV textures are currently concatenated into 4 channel RGBD images rather than RGB images, plus a separate displacement texture map. The --transparent and --greyscale options are currently not supported when calling the --render flag.
Both the --show_progress and --generate_interpolation flags from the original parent implementation are functional, but operate in the UV image space rather than in the render space.
This code is my Master Thesis repository. Although fully operational, the generator does not converge due to many instabilities encountered during the fragile GAN training, especially when learning a fourth displacement UV channel. Keep in mind that this implementation accordingly serves primarily as a basis for future research, rather than for direct usage. Further details on my work and the failure cases are accessible on my personal website.