Potential Enhancement of Temporal Coherence in SEINE's Video Generation

[yihong1120]

Dear SEINE Contributors,

I hope this message finds you well. I am reaching out to discuss an aspect of the SEINE model that I believe holds potential for further refinement, specifically regarding the temporal coherence in the generated video sequences.

Having perused your impressive work and the results it yields, I've noticed that while the spatial quality of individual frames is exceptional, there may be room for improvement in ensuring a seamless temporal transition between frames. Temporal coherence is paramount in video generation as it significantly impacts the perceived fluidity and realism of the motion depicted.

My suggestion entails a deeper exploration into the temporal aspect of the diffusion process. Could we possibly consider integrating a mechanism that explicitly accounts for motion estimation or optical flow between consecutive frames? This could enhance the model's ability to maintain consistency in dynamic elements, thus elevating the overall smoothness of the generated videos.

Additionally, I am curious whether there has been any exploration into the use of recurrent neural network architectures or transformer models that are adept at handling sequential data, which could potentially bolster the temporal stability of the generated content.

I believe that addressing this could further solidify SEINE's standing as a state-of-the-art model in video generation, and I would be thrilled to see any developments in this domain.

Thank you for your dedication to advancing the field of generative models. I look forward to your thoughts on this matter.

Best Regards,
yihong1120