qianlim/cape

Official implementation of CVPR2020 paper "Learning to Dress 3D People in Generative Clothing" https://arxiv.org/abs/1907.13615

cape's Issues

RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.

Hi, Qian~
I really appreciate your great work!
I want to run the demo, but met a problem:

when I ran the command:
python main.py --config configs/CAPE-affineconv_nz64_pose32_clotype32_male.yaml --mode demo

the terminal's log is:

Pre-computing mesh pooling matrices ..

loading pre-saved transform matrices...
Building model graph...

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

condition_pose_fc1: (126, 63)
condition_pose_fc2: (63, 32)
condition_clo_label_fc1: (4, 32)
condition_pose_fc1: (126, 63)
condition_pose_fc2: (63, 32)
condition_clo_label_fc1: (4, 32)

------------[Generator]------------
------------Encoder------------
encoder_conv1: (6890, 64), K=2
encoder_conv2: (3445, 64), K=2
encoder_conv3: (3445, 128), K=2
encoder_conv4: (1723, 128), K=2
encoder_conv5: (1723, 256), K=2
encoder_conv6: (862, 256), K=2
encoder_conv7: (862, 512), K=2
encoder_conv8: (862, 512), K=2
encoder_1x1conv: (862, 64), K=1
encoder_fc_mean: (55168, 64)
encoder_fc_logvar: (55168, 64)
------------Decoder------------
decoder_fc1: (128, 55168)
decoder_1x1conv: (862, 512), K=1
decoder_resblock_affine1: (862, 256), K=2
decoder_resblock_affine2: (862, 256), K=2
decoder_resblock_affine3: (1723, 128), K=2
decoder_resblock_affine4: (1723, 128), K=2
decoder_resblock_affine5: (3445, 64), K=2
decoder_resblock_affine6: (3445, 64), K=2
decoder_resblock_affine7: (6890, 32), K=2
decoder_resblock_affine8: (6890, 32), K=2
decoder_output: (6890, 3), K=2

----------[Discriminator]----------
conv1: (3445, 64), K=3
conv2: (1723, 64), K=3
conv3: (862, 128), K=3
conv4: (431, 128), K=3
pred_map: (431, 1), K=3

----------[Discriminator]----------
conv1: (3445, 64), K=3
conv2: (1723, 64), K=3
conv3: (862, 128), K=3
conv4: (431, 128), K=3
pred_map: (431, 1), K=3

For generative experiments:
condition_pose_fc1: (126, 63)
condition_pose_fc2: (63, 32)
condition_clo_label_fc1: (4, 32)
------------Encoder------------
encoder_conv1: (6890, 64), K=2
encoder_conv2: (3445, 64), K=2
encoder_conv3: (3445, 128), K=2
encoder_conv4: (1723, 128), K=2
encoder_conv5: (1723, 256), K=2
encoder_conv6: (862, 256), K=2
encoder_conv7: (862, 512), K=2
encoder_conv8: (862, 512), K=2
encoder_1x1conv: (862, 64), K=1
encoder_fc_mean: (55168, 64)
encoder_fc_logvar: (55168, 64)
------------Decoder------------
decoder_fc1: (128, 55168)
decoder_1x1conv: (862, 512), K=1
decoder_resblock_affine1: (862, 256), K=2
decoder_resblock_affine2: (862, 256), K=2
decoder_resblock_affine3: (1723, 128), K=2
decoder_resblock_affine4: (1723, 128), K=2
decoder_resblock_affine5: (3445, 64), K=2
decoder_resblock_affine6: (3445, 64), K=2
decoder_resblock_affine7: (6890, 32), K=2
decoder_resblock_affine8: (6890, 32), K=2
decoder_output: (6890, 3), K=2

2021-03-17 12:20:43.953960: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2021-03-17 12:20:44.184974: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x558d71c02640 executing computations on platform CUDA. Devices:
2021-03-17 12:20:44.185006: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): TITAN X (Pascal), Compute Capability 6.1
2021-03-17 12:20:44.185014: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (1): TITAN X (Pascal), Compute Capability 6.1
2021-03-17 12:20:44.203509: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3598130000 Hz
2021-03-17 12:20:44.204166: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x558d71c74fd0 executing computations on platform Host. Devices:
2021-03-17 12:20:44.204198: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): ,
2021-03-17 12:20:44.204352: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:01:00.0
totalMemory: 11.91GiB freeMemory: 11.12GiB
2021-03-17 12:20:44.204409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 1 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:03:00.0
totalMemory: 11.91GiB freeMemory: 11.77GiB
2021-03-17 12:20:44.206252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1
2021-03-17 12:20:44.209304: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-17 12:20:44.209335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 1
2021-03-17 12:20:44.209349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N Y
2021-03-17 12:20:44.209360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1: Y N
2021-03-17 12:20:44.209479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10813 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1)
2021-03-17 12:20:44.209972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 11446 MB memory) -> physical GPU (device: 1, name: TITAN X (Pascal), pci bus id: 0000:03:00.0, compute capability: 6.1)
2021-03-17 12:20:45.017010: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally

=============== Running demo: fix z, clotype, change pose ===============

Found 6 different pose, for each we generate 5 samples

2021-03-17 12:20:45.231372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1
2021-03-17 12:20:45.231460: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-17 12:20:45.231470: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 1
2021-03-17 12:20:45.231477: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N Y
2021-03-17 12:20:45.231483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1: Y N
2021-03-17 12:20:45.231553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10813 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1)
2021-03-17 12:20:45.231726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 11446 MB memory) -> physical GPU (device: 1, name: TITAN X (Pascal), pci bus id: 0000:03:00.0, compute capability: 6.1)
saving results as .obj files to /home/ang/CAPE-master/results/CAPE-affineconv_nz64_pose32_clotype32_male/sample_vary_pose...
Traceback (most recent call last):
File "main.py", line 109, in
demos.run()
File "/home/ang/CAPE-master/demos.py", line 335, in run
self.sample_vary_pose()
File "/home/ang/CAPE-master/demos.py", line 164, in sample_vary_pose
save_obj=self.save_obj, obj_dir=obj_dir)
File "/home/ang/CAPE-master/demos.py", line 324, in pose_result_onepose_multisample
self.smpl_model.body_pose[:] = torch.from_numpy(pose_params[i][3:])
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.

I've searched ways on google but still cannot solve it.
So I hope you can give me some advice. Thanks a lot!

Best wishes

ain't able to access your datasets

thanks for your great work, I am trying to train a new model, but the datasets are not able to be access

Fail to run demo due to missing data files

Traceback (most recent call last):
File "main.py", line 27, in
reference_mesh_file=reference_mesh_file)
File "/home/xxx/CAPE/lib/load_data.py", line 51, in init
self.load()
File "/home/xxx/CAPE/lib/load_data.py", line 62, in load
vertices_train = np.load(self.train_mesh_fn)
File "/home/xxx/anaconda3/envs/tf1.13/lib/python3.6/site-packages/numpy/lib/npyio.py", line 416, in load
fid = stack.enter_context(open(os_fspath(file), "rb"))
FileNotFoundError: [Errno 2] No such file or directory: '/home/xxx/CAPE/data/datasets/dataset_male_4clotypes/train/train_disp.npy'

How can I fix this? Thx!

Undefined name bodydata

Hello, found: CAPE/main.py:86:30: F821 undefined name 'bodydata'

two different group norm weights with same name in decoders

when I list the pretrained model using tf.train.list_variables, I get results as follows. You can see there are two different group norm weights used by same decoder_resblock such as generator_1/decoder/decoder_resblock_cmr1/group_norm/beta and generator/decoder/decoder_resblock_cmr1/group_norm/beta. I want to transfer the pretrained model from tensorflow to pytorch, so I am not sure which I should transfer to.

[('condition_clo_label/fc1/dense/bias', [8]),
('condition_clo_label/fc1/dense/bias/Momentum', [8]),
('condition_clo_label/fc1/dense/kernel', [4, 8]),
('condition_clo_label/fc1/dense/kernel/Momentum', [4, 8]),
('condition_pose/fc1/dense/bias', [63]),
('condition_pose/fc1/dense/bias/Momentum', [63]),
('condition_pose/fc1/dense/kernel', [126, 63]),
('condition_pose/fc1/dense/kernel/Momentum', [126, 63]),
('condition_pose/fc2/dense/bias', [24]),
('condition_pose/fc2/dense/bias/Momentum', [24]),
('condition_pose/fc2/dense/kernel', [63, 24]),
('condition_pose/fc2/dense/kernel/Momentum', [63, 24]),
('discriminator/prediction_map/weights', [256, 1]),
('discriminator/prediction_map/weights/Momentum', [256, 1]),
('discriminator/shared/conv1/bias', [1, 1, 64]),
('discriminator/shared/conv1/bias/Momentum', [1, 1, 64]),
('discriminator/shared/conv1/weights', [105, 64]),
('discriminator/shared/conv1/weights/Momentum', [105, 64]),
('discriminator/shared/conv2/bias', [1, 1, 64]),
('discriminator/shared/conv2/bias/Momentum', [1, 1, 64]),
('discriminator/shared/conv2/weights', [192, 64]),
('discriminator/shared/conv2/weights/Momentum', [192, 64]),
('discriminator/shared/conv3/bias', [1, 1, 128]),
('discriminator/shared/conv3/bias/Momentum', [1, 1, 128]),
('discriminator/shared/conv3/weights', [192, 128]),
('discriminator/shared/conv3/weights/Momentum', [192, 128]),
('discriminator/shared/conv4/bias', [1, 1, 128]),
('discriminator/shared/conv4/bias/Momentum', [1, 1, 128]),
('discriminator/shared/conv4/weights', [384, 128]),
('discriminator/shared/conv4/weights/Momentum', [384, 128]),
('generator/decoder/1x1-conv/weights', [64, 512]),
('generator/decoder/1x1-conv/weights/Momentum', [64, 512]),
('generator/decoder/decoder_resblock_cmr1/graph_conv/weights', [512, 256]),
('generator/decoder/decoder_resblock_cmr1/graph_conv/weights/Momentum',
[512, 256]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_1/weights', [544, 256]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_1/weights/Momentum',
[544, 256]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_2/weights', [256, 512]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_2/weights/Momentum',
[256, 512]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_input/weights',
[544, 512]),
('generator/decoder/decoder_resblock_cmr1/graph_linear_input/weights/Momentum',
[544, 512]),
('generator/decoder/decoder_resblock_cmr1/group_norm/beta', [544]),
('generator/decoder/decoder_resblock_cmr1/group_norm/beta/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr1/group_norm/gamma', [544]),
('generator/decoder/decoder_resblock_cmr1/group_norm/gamma/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr1/group_norm_1/beta', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_1/beta/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_1/gamma', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_1/gamma/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_2/beta', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_2/beta/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_2/gamma', [256]),
('generator/decoder/decoder_resblock_cmr1/group_norm_2/gamma/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr2/graph_conv/weights', [512, 256]),
('generator/decoder/decoder_resblock_cmr2/graph_conv/weights/Momentum',
[512, 256]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_1/weights', [544, 256]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_1/weights/Momentum',
[544, 256]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_2/weights', [256, 512]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_2/weights/Momentum',
[256, 512]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_input/weights',
[544, 512]),
('generator/decoder/decoder_resblock_cmr2/graph_linear_input/weights/Momentum',
[544, 512]),
('generator/decoder/decoder_resblock_cmr2/group_norm/beta', [544]),
('generator/decoder/decoder_resblock_cmr2/group_norm/beta/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr2/group_norm/gamma', [544]),
('generator/decoder/decoder_resblock_cmr2/group_norm/gamma/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr2/group_norm_1/beta', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_1/beta/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_1/gamma', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_1/gamma/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_2/beta', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_2/beta/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_2/gamma', [256]),
('generator/decoder/decoder_resblock_cmr2/group_norm_2/gamma/Momentum', [256]),
('generator/decoder/decoder_resblock_cmr3/graph_conv/weights', [256, 128]),
('generator/decoder/decoder_resblock_cmr3/graph_conv/weights/Momentum',
[256, 128]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_1/weights', [544, 128]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_1/weights/Momentum',
[544, 128]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_2/weights', [128, 256]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_2/weights/Momentum',
[128, 256]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_input/weights',
[544, 256]),
('generator/decoder/decoder_resblock_cmr3/graph_linear_input/weights/Momentum',
[544, 256]),
('generator/decoder/decoder_resblock_cmr3/group_norm/beta', [544]),
('generator/decoder/decoder_resblock_cmr3/group_norm/beta/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr3/group_norm/gamma', [544]),
('generator/decoder/decoder_resblock_cmr3/group_norm/gamma/Momentum', [544]),
('generator/decoder/decoder_resblock_cmr3/group_norm_1/beta', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_1/beta/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_1/gamma', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_1/gamma/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_2/beta', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_2/beta/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_2/gamma', [128]),
('generator/decoder/decoder_resblock_cmr3/group_norm_2/gamma/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr4/graph_conv/weights', [256, 128]),
('generator/decoder/decoder_resblock_cmr4/graph_conv/weights/Momentum',
[256, 128]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_1/weights', [288, 128]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_1/weights/Momentum',
[288, 128]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_2/weights', [128, 256]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_2/weights/Momentum',
[128, 256]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_input/weights',
[288, 256]),
('generator/decoder/decoder_resblock_cmr4/graph_linear_input/weights/Momentum',
[288, 256]),
('generator/decoder/decoder_resblock_cmr4/group_norm/beta', [288]),
('generator/decoder/decoder_resblock_cmr4/group_norm/beta/Momentum', [288]),
('generator/decoder/decoder_resblock_cmr4/group_norm/gamma', [288]),
('generator/decoder/decoder_resblock_cmr4/group_norm/gamma/Momentum', [288]),
('generator/decoder/decoder_resblock_cmr4/group_norm_1/beta', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_1/beta/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_1/gamma', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_1/gamma/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_2/beta', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_2/beta/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_2/gamma', [128]),
('generator/decoder/decoder_resblock_cmr4/group_norm_2/gamma/Momentum', [128]),
('generator/decoder/decoder_resblock_cmr5/graph_conv/weights', [128, 64]),
('generator/decoder/decoder_resblock_cmr5/graph_conv/weights/Momentum',
[128, 64]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_1/weights', [288, 64]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_1/weights/Momentum',
[288, 64]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_2/weights', [64, 128]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_2/weights/Momentum',
[64, 128]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_input/weights',
[288, 128]),
('generator/decoder/decoder_resblock_cmr5/graph_linear_input/weights/Momentum',
[288, 128]),
('generator/decoder/decoder_resblock_cmr5/group_norm/beta', [288]),
('generator/decoder/decoder_resblock_cmr5/group_norm/beta/Momentum', [288]),
('generator/decoder/decoder_resblock_cmr5/group_norm/gamma', [288]),
('generator/decoder/decoder_resblock_cmr5/group_norm/gamma/Momentum', [288]),
('generator/decoder/decoder_resblock_cmr5/group_norm_1/beta', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_1/beta/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_1/gamma', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_1/gamma/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_2/beta', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_2/beta/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_2/gamma', [64]),
('generator/decoder/decoder_resblock_cmr5/group_norm_2/gamma/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr6/graph_conv/weights', [128, 64]),
('generator/decoder/decoder_resblock_cmr6/graph_conv/weights/Momentum',
[128, 64]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_1/weights', [160, 64]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_1/weights/Momentum',
[160, 64]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_2/weights', [64, 128]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_2/weights/Momentum',
[64, 128]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_input/weights',
[160, 128]),
('generator/decoder/decoder_resblock_cmr6/graph_linear_input/weights/Momentum',
[160, 128]),
('generator/decoder/decoder_resblock_cmr6/group_norm/beta', [160]),
('generator/decoder/decoder_resblock_cmr6/group_norm/beta/Momentum', [160]),
('generator/decoder/decoder_resblock_cmr6/group_norm/gamma', [160]),
('generator/decoder/decoder_resblock_cmr6/group_norm/gamma/Momentum', [160]),
('generator/decoder/decoder_resblock_cmr6/group_norm_1/beta', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_1/beta/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_1/gamma', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_1/gamma/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_2/beta', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_2/beta/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_2/gamma', [64]),
('generator/decoder/decoder_resblock_cmr6/group_norm_2/gamma/Momentum', [64]),
('generator/decoder/decoder_resblock_cmr7/graph_conv/weights', [64, 32]),
('generator/decoder/decoder_resblock_cmr7/graph_conv/weights/Momentum',
[64, 32]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_1/weights', [160, 32]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_1/weights/Momentum',
[160, 32]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_2/weights', [32, 64]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_2/weights/Momentum',
[32, 64]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_input/weights',
[160, 64]),
('generator/decoder/decoder_resblock_cmr7/graph_linear_input/weights/Momentum',
[160, 64]),
('generator/decoder/decoder_resblock_cmr7/group_norm/beta', [160]),
('generator/decoder/decoder_resblock_cmr7/group_norm/beta/Momentum', [160]),
('generator/decoder/decoder_resblock_cmr7/group_norm/gamma', [160]),
('generator/decoder/decoder_resblock_cmr7/group_norm/gamma/Momentum', [160]),
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smpl_model_folder

Great work! To run the demo, we need to set the path to SMPL model folder.

python main.py --config configs/config.yaml --mode demo --vis_demo 1 --smpl_model_folder <path to SMPL model folder>

However, I didn't find the SMPL files in this repo(I may miss it). Could you provide the files or the links to download them? Thanks!

3D 虛擬渲染試衣 repository

裏面有相當不錯的示範代碼

Error while installing requirements on codelab

I am facing this error and not able to run the demo because of this. I tried looking at the closed issues to make sure I am not reporting the same issue again. Please let me know if I missed anything.

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchvision 0.13.1+cu113 requires torch==1.12.1, but you have torch 1.2.0 which is incompatible.
torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.2.0 which is incompatible.
torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.2.0 which is incompatible.
fastai 2.7.9 requires torch<1.14,>=1.7, but you have torch 1.2.0 which is incompatible.

Not able to run demo.

None

RuntimeError: size of dimension does not match previous size, operand 1, dim 2

I followed all your README steps to build the project.
Using all your pre-trained models and corresponding config files will get the RuntimeError.

the error happened on this line of code:

where the shape of betas and shape_disps are (1, 10) and (6890, 3, 300).
I want to know where does the error happen? Thanks a lot!

Pytorch in requirements

Hi! Great work and thank you for making the code public.
I think the torch==1.2.0 is an overhead in the requirements.txt file. Would be helpful for the end-user if the over head can be removed.
TIA

No module named 'psbody'

WoW! Awesome work!
I have some trouble with your released code:

1. ModuleNotFoundError: No module named 'psbody'. I did'n find the psbody.py

  File "main.py", line 8, in <module>
    from psbody.mesh import Mesh
ModuleNotFoundError: No module named 'psbody'

2. I'm pretty interested in the SMPL and CAPE, so could you send me a copy of code about SMPL in tf? It'll be important for me to learn the CAPE.
   Thanks for sharing the gread work!

Custom 3d data.

Hi,
I have some 3d scans of my own clothes. I want to extract z-parameters for those scans. I did a bit of research and found out that my garment scans must be registered to your data. May i get some help on this registration task? How can i register my own scans to any dataset that is publicily available? A sample code would be much appreciated. If that wont be possible, can you give me the steps people use for doing this non-rigid mesh registration task.

Thanks.

Mapping 3D T-shirt to Z variable

How can we map an unknown T-Shirt style to the Z variable?
Basically, Instead of sampling, I want to dress a person in a specific 3D garment (say T-shirt). Is it possible with the CAPE ?.

How to use my image data to get the cape model

If I know the pose parameters, how can I use the code to obtain the cape model? I am not sure where I should modify the code. I look forward to your reply. I appreciate it.

Demo can save the results of obj, but it cannot display the results

Hi, I can run your program and save the results of the model, but it doesn't seem to be able to visualize the model effect. The display effect is a black background, and there is no rendered model. It may be the problem of meshviewers in psbody, but I used different versions of psbody, and the problem is still not solved. More specifically, it seems that meshviewers are not initialized, but it is not clear how to solve it

viewer = MeshViewers(shape=(1, 2), titlebar=titlebar):