Comments (1)
Hi SandhyaLaxmi. Thanks for being interested in our work.
For the Q1, you can use it like python ./scripts/flow_regression_module.py --gpu_ids 0 --id model_id
. Also, train_flow_regression_module.py will perform testing and output testing information after each training epoch.
For Q2, I think other segmentation data will also help. The segmentation information is used in the training of the flow regression module to penalize pixels warped to incorrect regions, which we think is like hard negative mining. The reason we use SMPL to generate segmentation data instead of using other segmentation model/dataset is to avoid involving extra knowledge.
For Q3, you can test pose_transfer_0.1 just like other model by python scripts/test_pose_transfer.py --gpu_ids 0 --id PoseTransfer_0.1 --which_epoch best --save_output
. Please note that pose_transfer_0.1 has a normal U-Net architecture without a dual encoder.
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Related Issues (14)
- small batch_size causes bad result HOT 2
- No util.image exist. The ninth line in visualizer.py should be deleted HOT 1
- How to generate 3d ground truth HOT 17
- Test on other dataset
- How to generate seg data? HOT 2
- How to select pretrained G epoch when training the full model HOT 1
- silhouette24, silhouette6 not giving correct segmentation
- What is the variable d? HOT 1
- download links fail HOT 2
- The download link for the DeepFashion dataset fails HOT 1
- Python 3 Support? HOT 1
- how to test on our own pictures?
- About pixel warping HOT 2
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