Comments (1)
For real image, we have precomputed the transform matrix using ground-truth landmarks, so we use the self.trans_m for real image feature extraction directly. For predicted faces, we have two options. We can utilize the predicted facial landmarks to calculate the matrix or we can simply use the predefined one.
from deep3dfacerecon_pytorch.
Related Issues (20)
- visualize the face mesh without texture and color HOT 2
- RuntimeError: Cuda error: 304[cudaGraphicsGLRegisterBuffer(&s.cudaPosBuffer, s.glPosBuffer, cudaGraphicsRegisterFlagsWriteDiscard);] HOT 1
- ValueError: Unknown CUDA arch (8.9) or GPU not supported
- On the issue of differences in training visualization and testing effectiveness
- False error in training
- FileNotFoundError: [Errno 2] No such file or directory: './checkpoints/face_recon/epoch_latest.pth'
- Use this project with recent GPU ? HOT 6
- Open mouth HOT 2
- Pretrained model
- No module named 'models.arcface_torch' HOT 1
- RuntimeError: CUDA error: no kernel image is available for execution on the device
- ImportError: DLL load failed HOT 2
- RuntimeError: Error building extension 'nvdiffrast_plugin_gl': ninja: error: build.ninja:3: lexing error HOT 1
- What does align_img do in test.py? HOT 1
- error while running HOT 1
- UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd3 in position 1454: invalid continuation byte
- ImportError: libtorch_cpu.so: undefined symbol: iJIT_IsProfilingActive HOT 5
- resnet50-0676ba61.pth
- Thank you for your work. There is no result showing in preprocess_in_th_wild.py
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from deep3dfacerecon_pytorch.