kebii / tapmo Goto Github PK
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License: MIT License
Hi,
Thanks for this amazing work! I was wondering when you are planning on releasing the training code.
python3 -m sample.generate_handle_motion --model_path ../weights/diffusion_model_latest.pt --arch trans_dec --emb_trans_dec False --dataset t6d_mixrig --char_feature_path ../demo/shape_features/001.npy --save_path ../demo/motion/motion_3 --text_prompt "walk forward and turn right."
Loading model args from [../weights/args.json]
Warning: was not able to load [char_feature_path], using default value [../demo/shape_features/001.npy] instead.
Warning: was not able to load [save_path], using default value [../demo/motion/motion_3] instead.
Loading dataset...
get dateset: t6d_mixrig
Loading dataset t6d_mixrig ...
Traceback (most recent call last):
File "/home/zjp/anaconda3/envs/sfpt/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/zjp/anaconda3/envs/sfpt/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/zjp/TapMo/shape_diffusion/sample/generate_handle_motion.py", line 205, in <module>
main()
File "/home/zjp/TapMo/shape_diffusion/sample/generate_handle_motion.py", line 66, in main
data = load_dataset(args, max_frames, n_frames)
File "/home/zjp/TapMo/shape_diffusion/sample/generate_handle_motion.py", line 195, in load_dataset
data = get_dataset_loader(name=args.dataset,
File "/home/zjp/TapMo/shape_diffusion/data_loaders/get_data.py", line 50, in get_dataset_loader
dataset = get_dataset(name, num_frames, split, hml_mode)
File "/home/zjp/TapMo/shape_diffusion/data_loaders/get_data.py", line 43, in get_dataset
dataset = DATA(split=split, num_frames=num_frames, mode=hml_mode)
File "/home/zjp/TapMo/shape_diffusion/data_loaders/humanml/data/dataset_t6d_mixrig.py", line 874, in __init__
self.mean = np.load(pjoin(datapath, 'Mean_30p_6d.npy'))[None,]
File "/home/zjp/anaconda3/envs/sfpt/lib/python3.9/site-packages/numpy/lib/npyio.py", line 427, in load
fid = stack.enter_context(open(os_fspath(file), "rb"))
FileNotFoundError: [Errno 2] No such file or directory: '../datasets/HumanML3D/Mean_30p_6d.npy'
Thanks for sharing code!
I download the datasets.zip from google drive but missing the files.
Fantastic work! Will the code be open sourced? and when?
python3 -m sample.generate_handle_motion --model_path ../weights/diffusion_model_latest.pt --arch trans_dec --emb_trans_dec False --dataset t6d_mixrig --char_feature_path ../demo/shape_features/001.npy --save_path ../demo/motion/motion_3 --text_prompt "walk forward and turn right."
Loading model args from [../weights/args.json]
Traceback (most recent call last):
File "/home/zjp/anaconda3/envs/sfpt/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/zjp/anaconda3/envs/sfpt/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/zjp/TapMo/shape_diffusion/sample/generate_handle_motion.py", line 205, in <module>
main()
File "/home/zjp/TapMo/shape_diffusion/sample/generate_handle_motion.py", line 19, in main
args = generate_args()
File "/home/zjp/TapMo/shape_diffusion/utils/parser_util_tapmo.py", line 226, in generate_args
return parse_and_load_from_model(parser)
File "/home/zjp/TapMo/shape_diffusion/utils/parser_util_tapmo.py", line 22, in parse_and_load_from_model
assert os.path.exists(args_path), 'Arguments json file was not found!'
AssertionError: Arguments json file was not found!
Thanks for sharing the inference code!
I download the weights.zip
from google drive and unzip it into the folder but missing args.json
file to get the arguments, could you provide it?
Hi,
I checked the demo results' obj and it looks great. Tho I do have some customization request:
How to run TapMo on a customized model? i.e. given an customized .obj file, not from the demo. I notice that you use the shape_features/001.npy at step 3, not a standard .obj file.
How to have more than currently 120 .obj outputs?
Appreciate your help!
How do you obtain the skeleton-driven mesh mentioned in the paper? The article doesn't seem to explain this in great detail.
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