diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 9653787..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 aashishrai3799 - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index 154275b..0000000 --- a/README.md +++ /dev/null @@ -1,63 +0,0 @@ - -# Official implementation of Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance - -Fariborz Teherkhani, Aashish Rai*, Shaunak Srivastava*, Quankai Gao*, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim - -(* equal contribution) - -### More details coming soon! - - -[[Project Page]](https://aashishrai3799.github.io/3DFaceCAM) [[Video]](#) [[Colab Demo]](#) [[Demo Code]](#) [[Arxiv]](#) - - -## Install packages -See [`install.md`](docs/install.md) - -## Testing - -Download pre-trained weights and put the "checkpoints" folder in the main directory. [[Link]](https://drive.google.com/file/d/1hK31wVAoieRiVFydPxnx0MVpx6AnWN1-/view?usp=sharing) - -- Generate 3D Faces (mesh and texture) - ``` - python generate_faces.py - ``` - -- Generate meshes only - ``` - python test_gan3d.py - ``` - -- Generate textures only - ``` - python test_texture.py - ``` - -## Train your own model - -### Preprocess data - - python preprocess.py (Coming Soon) - - -### Start training - -- Shape - ``` - Train AE - python train_ae.py - ``` - ``` - Train GAN - python train_gan3d.py - ``` - -- Texture - ``` - Train P-GAN - python train_texture.py --init_step 1 --batch_size 128 - ``` - - - - diff --git a/data/reduced_train_data_130.npy b/data/reduced_train_data_130.npy deleted file mode 100644 index da92bae..0000000 Binary files a/data/reduced_train_data_130.npy and /dev/null differ diff --git a/data/template_mesh.obj b/data/template_mesh.obj deleted file mode 100644 index 3efd21b..0000000 --- a/data/template_mesh.obj +++ /dev/null @@ -1,104984 +0,0 @@ -mtllib 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a/data/zid_dictionary.pkl and /dev/null differ diff --git a/generate_faces.py b/generate_faces.py deleted file mode 100644 index a108ecb..0000000 --- a/generate_faces.py +++ /dev/null @@ -1,105 +0,0 @@ -import yaml -from train_gan3d import shape_GAN -import os -from test_gan3d import Test -import torch - -from test_texture import test_texture -from texture_model.progan_modules import Generator, Discriminator -from texture_model.assets.texture_processing import add_tex -from data.edit_obj import get_mtl -import argparse - - - -def generate_shapes(num_imgs=1, exp_list=[1,2]): - - with open("config.yml","r") as cfgfile: - cfg = yaml.safe_load(cfgfile) - - gan = shape_GAN(cfg, device, args) - - path_d = args.path_decoder - path_gid = args.path_gid - path_gexp = args.path_gexp - - with torch.no_grad(): - - test = Test(gan, args) - test.load_models(path_d=path_d, path_gid=path_gid, path_gexp=path_gexp) - - for i in range(num_imgs): - test.set_z_id(torch.randn(1,20).to(device)) - test.generate(str(i), intensities=True, save_obj=True, render=False, exp_list=exp_list) - - -def generate_tex(num_imgs=1, exp_list=[1,2]): - - input_code_size = 128 - channel = 256 - - g_running = Generator(in_channel=channel, input_code_dim=input_code_size+20+20, pixel_norm=False, tanh=False) - g_running = torch.nn.DataParallel(g_running) - - - g_running = g_running.to(device) - - model_dir = 'checkpoints/texture_models/' - number = '142000' - - g_running.load_state_dict(torch.load(model_dir + 'checkpoint/' + number + '_g.model'), strict=False) - g_running = torch.nn.DataParallel(g_running) - g_running.train(False) - - test_texture(g_running, num_imgs=num_imgs, exp_list=exp_list, input_code_size=input_code_size, device=device, alpha=1, out_path='results/', zid_dict_path='data/zid_dictionary.pkl') - - - -if __name__ == '__main__': - - - ### Expressions list for reference - - # ['0_neutral.jpg', '1_smile.jpg', '2_mouth_stretch.jpg', '3_anger.jpg', '4_jaw_left.jpg', '5_jaw_right.jpg', '6_jaw_forward.jpg', '7_mouth_left.jpg', '8_mouth_right.jpg', '9_dimpler.jpg', '10_chin_raiser.jpg', '11_lip_puckerer.jpg', '12_lip_funneler.jpg', '13_sadness.jpg', '14_lip_roll.jpg', '15_grin.jpg', '16_cheek_blowing.jpg', '17_eye_closed.jpg', '18_brow_raiser.jpg', '19_brow_lower.jpg'] - - - - parser = argparse.ArgumentParser() - - parser.add_argument('--results', type=str, default='results/') - parser.add_argument('--path_decoder', type=str, default='checkpoints/ae/Decoder/2000') - parser.add_argument('--path_gid', type=str, default='checkpoints/gan3d/Generator_Checkpoint_id/8.0') - parser.add_argument('--path_gexp', type=str, default='checkpoints/gan3d/Generator_Checkpoint_exp/8.0') - parser.add_argument('--checkpoints_path', type=str, default='checkpoints/gan3d/') - parser.add_argument('--zid_dict', type=str, default='data/zid_dictionary.pkl') - - args = parser.parse_args() - - - - device = torch.device("cuda") - - num_imgs = 1 - exp_list = [1,2] #[Smile, Mouth Stretch] - - print('Generating', num_imgs, 'Faces...') - - ### GENERATE SHAPES ### - - generate_shapes(num_imgs, exp_list) - - ### GENERATE TEXTURES ### - - generate_tex(num_imgs, exp_list) - - add_tex.add_texture_template(in_path='results/', base_path = 'texture_model/assets/texture_processing/base_tex.npy', out_resolution=1024) - - ### GENERATE MTL ### - - get_mtl(in_path='results/') - - - - - - diff --git a/models.zip b/models.zip new file mode 100644 index 0000000..95bc2f9 Binary files /dev/null and b/models.zip differ diff --git a/pytorch-AE-master.zip b/pytorch-AE-master.zip new file mode 100644 index 0000000..6a8bfbc Binary files /dev/null and b/pytorch-AE-master.zip differ diff --git a/rendering.zip b/rendering.zip new file mode 100644 index 0000000..6be10c8 Binary files /dev/null and b/rendering.zip differ diff --git a/test_gan3d.py b/test_gan3d.py deleted file mode 100644 index 4c7d0fe..0000000 --- a/test_gan3d.py +++ /dev/null @@ -1,144 +0,0 @@ -import os -import sys -import yaml -import argparse -sys.path.append(".") - -import torch -import torch.nn as nn - -import numpy as np - -from shape_model.mesh_obj import mesh_obj -from shape_model.architectures import Decoder, Generator -from shape_model.gan3d.utils.utils import vertex_to_mesh, get_template_verts, load_label -from train_gan3d import shape_GAN - -class Test: - def __init__(self, gan_model, args): - self.gan = gan_model - self.result_path = args.results - self.decoder = None - self.label_dict = load_label() - self.device = self.gan.device - self.template_verts = torch.from_numpy(get_template_verts()).to(self.device) - self.face_v = torch.from_numpy(np.load('./data/face_v.npy')).to(dtype=torch.float32).to(self.device).unsqueeze(0) - 1 - - self.z_noise = torch.randn(1,5).to(self.device) - self.z_id = torch.randn(1,20).to(self.device) - self.z_id_style = torch.randn(1,20).to(self.device) - self.z_exp = torch.nn.functional.one_hot(torch.tensor([0]), num_classes=20).to(self.device) - - - def set_z_noise(self,z_noise): - self.z_noise = z_noise - - - def set_z_id(self,z_id): - self.z_id = z_id - - - def set_z_id_style(self,z_id_style): - self.z_id_style = z_id_style - - - def set_z_exp(self,z_exp): - self.z_exp = z_exp - - - def load_models(self, path_d, path_gid, path_gexp): - self.decoder = Decoder(num_features=130, output_size=78951) - self.decoder.load_state_dict(torch.load(path_d, map_location=self.device)) - self.decoder = self.decoder.to(self.device) - - self.gan.generator.generator_id.load_state_dict(torch.load(path_gid, map_location=self.device)) - self.gan.generator.generator_exp.load_state_dict(torch.load(path_gexp, map_location=self.device)) - - - def generate_emb(self, z_noise, z_id, z_id_style, z_exp): - test_noise_id = torch.cat((z_noise, z_id), 1) - test_noise_ex = torch.cat((z_noise, z_id_style, z_exp), 1) - emb_id, emb_ex = self.gan.generator(test_noise_id, test_noise_ex) - - return emb_id, emb_ex - - - def decode_mesh(self, emb_id, emb_ex): - z = torch.cat((emb_id,emb_ex), 1) - return (self.decoder(z)+ self.template_verts).reshape(26317,3) - - - def generate_intensity(self, filename, low=0, high=1.5, num_steps=15, exp_list=range(0,20), save_obj=True, render=False): - for i in exp_list: - subfolder = os.path.join(self.result_path, filename, str(i)+'_'+self.label_dict[i]) - z_exp = torch.nn.functional.one_hot(torch.tensor([i]), num_classes=20).to(self.device) - - meshes = [] - for count,level in enumerate(np.linspace(low,high,15)): - emb_id, emb_ex = self.generate_emb(self.z_noise,self.z_id,self.z_id_style, (level*z_exp)) - mesh_verts = self.decode_mesh(emb_id, emb_ex) - meshes.append(mesh_verts) - - if save_obj: - vertex_to_mesh(mesh_verts, count, subfolder) - if render: - from shape_model.gan3d.renderer_pt3d import renderfaces - renderfaces(torch.stack(meshes).float(), self.face_v, subfolder+'.png',self.device) - - - def generate(self, filename, intensities=False, low=0, high=1.5, num_steps=15, exp_list=range(0,20), save_obj=True, render=False): - if not os.path.exists(self.result_path): - os.makedirs(self.result_path) - - if not intensities: - meshes = [] - for i in exp_list: - subfolder = os.path.join(self.result_path, filename) - z_exp = torch.nn.functional.one_hot(torch.tensor([i]), num_classes=20).to(self.device) - - emb_id, emb_ex = self.generate_emb(self.z_noise, self.z_id, self.z_id_style,z_exp) - mesh_verts = self.decode_mesh(emb_id, emb_ex) - meshes.append(mesh_verts) - - if save_obj: - vertex_to_mesh(mesh_verts, str(i)+'_'+self.label_dict[i], subfolder) - if render: - from shape_model.gan3d.renderer_pt3d import renderfaces - renderfaces(torch.stack(meshes).float(), self.face_v, subfolder+'_image.png',self.device) - else: - self.generate_intensity(filename,low,high,num_steps,exp_list,save_obj,render) - - -if __name__ == '__main__': - device = 'cuda:0' - torch.device(device) - - parser = argparse.ArgumentParser() - - parser.add_argument('--results', type=str, default='results/') - parser.add_argument('--path_decoder', type=str, default='checkpoints/ae/Decoder/2000') - parser.add_argument('--path_gid', type=str, default='checkpoints/gan3d/Generator_Checkpoint_id/8.0') - parser.add_argument('--path_gexp', type=str, default='checkpoints/gan3d/Generator_Checkpoint_exp/8.0') - parser.add_argument('--checkpoints_path', type=str, default='checkpoints/gan3d/') - parser.add_argument('--zid_dict', type=str, default='data/zid_dictionary.pkl') - - args = parser.parse_args() - - with open("config.yml","r") as cfgfile: - cfg = yaml.safe_load(cfgfile) - - gan = shape_GAN(cfg, device, args) - - path_d = args.path_decoder - path_gid = args.path_gid - path_gexp = args.path_gexp - - with torch.no_grad(): - test = Test(gan, args) - test.load_models(path_d=path_d, path_gid=path_gid, path_gexp=path_gexp) - - for i in range(1): - test.set_z_noise(torch.randn(1,5).to(device)) - test.set_z_id(torch.randn(1,20).to(device)) - test.set_z_id_style(torch.randn(1,20).to(device)) - test.generate(str(i), save_obj=True, render=False) diff --git a/test_texture.py b/test_texture.py deleted file mode 100644 index 7e66e9c..0000000 --- a/test_texture.py +++ /dev/null @@ -1,123 +0,0 @@ -import numpy as np -import random -import torch -import os -import matplotlib.pyplot as plt -from torchvision import utils -import pickle - -from texture_model.assets.texture_processing import add_tex -from texture_model.progan_modules import Generator, Discriminator - -class my_dictionary(dict): - - # __init__ function - def __init__(self): - self = dict() - - # Function to add key:value - def add(self, key, value): - self[key] = value - - -def check_folder(path): - if not os.path.exists(path): - os.mkdir(path) - - - -def test_texture(g_running, num_imgs=10, exp_list=[1,2], input_code_size=512, device='cuda:0', alpha=1.00, out_path='texture_results/', zid_dict_path='assets/zid_dictionary.pkl'): - - step = 7 - - id_to_exp = ['0_neutral.jpg', '1_smile.jpg', '2_mouth_stretch.jpg', '3_anger.jpg', '4_jaw_left.jpg', '5_jaw_right.jpg', '6_jaw_forward.jpg', '7_mouth_left.jpg', '8_mouth_right.jpg', '9_dimpler.jpg', '10_chin_raiser.jpg', '11_lip_puckerer.jpg', '12_lip_funneler.jpg', '13_sadness.jpg', '14_lip_roll.jpg', '15_grin.jpg', '16_cheek_blowing.jpg', '17_eye_closed.jpg', '18_brow_raiser.jpg', '19_brow_lower.jpg'] - - with open(zid_dict_path, 'rb') as f: - zid_dict = pickle.load(f) - - #npy_file_name = '../create_videos/results/interpolate_id/20.npy' - #zid_dict = np.load(npy_file_name) - - check_folder(out_path) - - for num in range(num_imgs): - - id_path = os.path.join(out_path, str(num)) - check_folder(id_path) - - for exp in exp_list: - #for exp in [0,1,2,17]: # For specific expressions - - exp_enc_test = np.zeros((1,20), dtype='int') - exp_enc_test[0,exp] = 1 - - exp_path = os.path.join(id_path, id_to_exp[exp].split('.')[0]) - check_folder(exp_path) - - for intensity in range(15): - - with torch.no_grad(): - - # For EXTRAPOLATION use this latent code - - z_noise = torch.randn(1, input_code_size) - #z_id = torch.randn(1, 20) # if random z_id - z_id = np.reshape(zid_dict[num+1], (1,20)) # if predefined z_id - z_exp = torch.from_numpy(exp_enc_test)*0.1*intensity - latent_code = torch.cat((z_noise, z_id, z_exp),1) - - #For INTERPOLATION use this latent code - - '''z_noise = (torch.randn(1, input_code_size) - z_id = torch.from_numpy(np.reshape(zid_dict[intensity], (1,20))) - z_exp = torch.from_numpy(exp_enc_test) - latent_code = torch.cat((z_noise, z_id, z_noise),1)''' - - images = g_running(input=latent_code.to(device), step=step, alpha=alpha).data.cpu() - - utils.save_image(images, os.path.join(exp_path, str(intensity)) + '.jpg', nrow=1, normalize=True, range=(0, 1)) - - print('Generating ID:', num, ',Exp:', exp_enc_test, end='\r') - - #add_tex.add_texture_template(in_path='texture_results/', out_resolution=1024) - - - -if __name__ == '__main__': - - device = torch.device("cuda") - - input_code_size = 128 - channel = 256 - - g_running = Generator(in_channel=channel, input_code_dim=input_code_size+20+20, pixel_norm=False, tanh=False) - g_running = torch.nn.DataParallel(g_running) - - os.environ["CUDA_VISIBLE_DEVICES"]="0" - - g_running = g_running.to(device) - - model_dir = 'checkpoints/texture_models/' - number = '142000' - - g_running.load_state_dict(torch.load(model_dir + 'checkpoint/' + number + '_g.model'), strict=False) - g_running = torch.nn.DataParallel(g_running) - g_running.train(False) - - test_texture(g_running, num_imgs=2, input_code_size=input_code_size, device=device, alpha=1, out_path='texture_results2/', zid_dict_path='data/zid_dictionary.pkl') - - add_tex.add_texture_template(in_path='texture_results2/', base_path = 'texture_model/assets/texture_processing/base_tex.npy', out_resolution=1024) - - - - - - - - - - - - - -