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Have you tried other experiments such as not disentangle algorithms to provide some straightforward comparisons like image?
As I think, the best point of this method is less training than other methods and is quite like AnimateDiff and stablediffusion-controlnet-pose and Personalization methods (which I believe lots of people have already used the combination of these as mentioned in your paper) but this work may not be so much convincing to say dissentanglement is the key to high-fidelity temporally coherent.
The text was updated successfully, but these errors were encountered:
That's a good point. We've conducted some preliminary experiments but haven't consolidated the results yet. We will update here as soon as we arrive at a more concrete conclusion
Have you tried other experiments such as not disentangle algorithms to provide some straightforward comparisons like image?
As I think, the best point of this method is less training than other methods and is quite like AnimateDiff and stablediffusion-controlnet-pose and Personalization methods (which I believe lots of people have already used the combination of these as mentioned in your paper) but this work may not be so much convincing to say dissentanglement is the key to high-fidelity temporally coherent.
The text was updated successfully, but these errors were encountered: