Hi, this is Frances.
The project of my page is modified from that of Guillermo Garcia-Hernando with Leonids Jekyll Theme. All credit goes to them, the code is with a MIT licence.
This is the original implementation of the paper Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos
License: MIT License
Hi, this is Frances.
The project of my page is modified from that of Guillermo Garcia-Hernando with Leonids Jekyll Theme. All credit goes to them, the code is with a MIT licence.
I think there's incompatible function in preprocess_utils.py 38 line.
Original
frame_path_dst = os.listdir(fhb_rgb_dst, subj, action, seq, "color", frame)
After
frame_path_dst = os.path.join(fhb_rgb_dst, subj, action, seq, "color", frame)
Hello, may I ask if the FPHA dataset of your HTT model is open-sourced for training
fixed it by replacing it with glob2
Hello, thanks for share !
I encountered an error while training:
Traceback (most recent call last):
File "/home/zhangjiabao/data/1/zhangjiabao/mesh/HTT/train.py", line 238, in
main(args)
File "/home/zhangjiabao/data/1/zhangjiabao/mesh/HTT/train.py", line 131, in main
epochpass.epoch_pass(
File "/home/zhangjiabao/data/1/zhangjiabao/mesh/HTT/netscripts/epochpass.py", line 50, in epoch_pass
loss, results, losses = model(batch)
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py", line 168, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py", line 178, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/_utils.py", line 434, in reraise
raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/home/zhangjiabao/data/1/zhangjiabao/miniconda3/envs/handmesh/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/zhangjiabao/data/1/zhangjiabao/mesh/HTT/models/htt.py", line 169, in forward
batch_seq_ain_feature=flatten_ain_feature.contiguous().view(-1,self.ntokens_action,flatten_ain_feature.shape[-1])
RuntimeError: shape '[-1, 128, 512]' is invalid for input of size 16384
The shape doesn't match. I think one of the parameters is self.ntokens_action, do I think the initial settings for this parameter are customized?
May I know how to solve the problem of parameter matching.
Hello, I want to use my camera to test.Do you have real-time demo? Thanks!
Thank you so much for your work!
I'm wondering if this model can be applied to any custom data without GT labels? Could you offer code for reference? Thanks a lot for your reply!
hello!for training,if i use 3090Ti,how many hours for an epoch
Hey, thank you for your work.
In utils.py->to25Dbranch, why inp_res = [256,256], but inp_res=[480, 270]? And, why the trans_factor =100, scale_factor = 0.0001?
Thank you, looking for your reply!
Hello, thanks for share !
I wonder if the model only support first-person view?
And is there any code for inferencing in wild video?
Hello, the link you have provided for the pre-trained model doesn't seem to be working. Could you please update it?
Thank you very much for your work!
I am currently facing some issues
how can I report the hand MEPE and action recall rate by referring to our submitted results?
how to generate the handposes.json file from eval.py
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