Comments (1)
Great question! This was indeed a challenge during our research. Although EG3D had published their code, they did not provide an explanation for the manual adjustments they made to their camera poses in their code. This was particularly problematic since our face alignment process was completely different. For the talking face task, we cropped the videos based solely on the bounding box calculated in the first frame, meaning that the subsequent frames were not aligned. Given this approach, determining how to model the rotation and translation was complex, especially when the EG3D camera convention was potentially misleading. We had no option but to manually adjust these parameters in the context of a talking face setting.
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Related Issues (20)
- How to transfer expression from target img to source img HOT 11
- Quantitative Evaluation HOT 12
- Difference between code and paper HOT 1
- ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 0 (pid: 150437) of binary: /data1/anconda3/envs/otavatar/bin/python HOT 2
- When do you plan to upload the data preprocessing script? HOT 1
- Could you please upload the remaining preprocessing scripts?
- Can you please tell on what operating system is this code running ? HOT 1
- How to makes face animation with specified image HOT 4
- About downloading processed data HOT 2
- Question about 'FaceTrainer' object has no attribute 'net_G_module'
- question about emotion code , ws_stdv HOT 4
- No arcface_resnet18.pth found in arcface-pytorch HOT 1
- About training consumption and inference speed HOT 1
- License file HOT 1
- Makes face animation with specified image HOT 3
- About requirement.yaml
- metric code about cross-identity reenactment HOT 1
- Multi-GPU trainning error (RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cuda:3) HOT 1
- Pretrained model batchsize and gpus HOT 1
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