Comments (7)
Hi, thanks for your interest in our work. Have you used the EFT labels? And how about the performance of the HMR baseline? It is also recommended to monitor the performance of 3DPW after every epoch.
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Thanks for answer, i use the eft labels provided in Pymaf to train the pymafx. I want to make sure that the models' performance on 3dpw is only traind on coco and other 2d keypoints dataset?
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Hi, 3D mocap datasets such as Human3.6M and MPI-INF-3DHP are also needed.
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Hi, so the train have two stage. 1.train on 2d dataset 2.train on whole dataset. After these training, the model is evaluated on 3dpw?
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Yes.
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Hi, I hope you could provide a more detailed reproduction plan. For example, first train on the COCO and MPII data sets, and then retrain the entire data 2d+3d data set on the best checkpoints. Although I can tell you Find their detailed training methods in PARE. But in your paper, I can hardly find any specific training methods after you join the MPII data set.
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Thank you for your advice.
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Related Issues (20)
- Flame downsampling HOT 6
- How to save mesh without background?
- I ran into this problem when running a demo with my own video HOT 3
- bug when saveobj
- Training details
- How to read smplx_params output by smplx.SMPLX? HOT 4
- RuntimeError: CUDA out of memory when dealing folder with large amount of pics
- How to use multiple gpus to train PyMAF-X
- Some questions about real-time 3d pose estimation.
- USE_IWP_CAM:False, some error
- Extracting some parameters from the model
- Problem with output video
- model size mismatch
- About body_pose
- Evaluation code release
- Training procedure too slow
- Joint pose
- How to process AGORA dataset?
- Prepare data for training
- openpifpaf installation error HOT 2
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