Comments (5)
We did not include vox, because there is no benifit in using current model. As explained in the paper. The checkpoint and config for vox is still provided.
from articulated-animation.
Since you actually went through effort of evaluating it, even if it didn't improve, there are insights that can develop from the new framework on such data. Your work so it is your call. Thanks for responding.
from articulated-animation.
We did not include vox, because there is no benifit in using current model. As explained in the paper. The checkpoint and config for vox is still provided.
@AliaksandrSiarohin
We evaluated the vox256.pth that provided in this repository on our own face-related test set. The quantitative result shows that this model is better than the vox-adv-cpk from FOMM.
Can you speculate on the reason why the model results become better?
from articulated-animation.
Just a speculation. This model has better generalisation when estimating affine transformations, you can see explanation for this phenomenon in the toy experiment section. Because we evaluate model on faces we did not observe an improvement, since vox dataset is already large and generalisation is not a problem. Since your dataset is face-like, generalization may still be the issue.
from articulated-animation.
Thank you for your explanation! I really like your this work, and MonKeyNet and FOMM too.
from articulated-animation.
Related Issues (20)
- error when training avd with videos HOT 1
- What version of CUDA, cuDNN are you using? HOT 6
- produce smooth video results
- Some questions about Teddataset HOT 1
- your colab brings a ton of errors.
- Why do your images work pretty well but my images make the result very bad
- When training, does the input image pixel size need to be consistent?
- What source images do you use to compute IoU for co-part segmentation (Section 4.5) in the paper?
- from skimage.draw import circle ImportError: cannot import name 'circle' from 'skimage.draw' (/home/onur/.local/lib/python3.9/site-packages/skimage/draw/__init__.py) HOT 4
- Discriminator module
- Git LFS quota limit HOT 2
- Dataset request
- RuntimeError: Error(s) in loading state_dict for Generator: size mismatch for pixelwise_flow_predictor.hourglass.encoder.down_blocks.0.conv.weight: copying a param with shape torch.Size([128, 44, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 84, 3, 3]). HOT 4
- How to get the ted-youtube384.pth ? HOT 1
- Disentanglement is not good
- Regarding the start and End time of the videos HOT 1
- Not able to fine tune ted-youtube384.pth model. HOT 9
- what should i do to test the cloth swap
- youtube-dl problem HOT 2
- Question: which Python 3 versions can I use with this repo?
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from articulated-animation.