| --- |
| pipeline_tag: image-to-video |
| --- |
| |
|
|
| # StreamingSVD |
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| **[StreamingSVD: Consistent, Dynamic, and Extendable Image-Guided Long Video Generation]()** |
| </br> |
| Roberto Henschel, |
| Levon Khachatryan, |
| Daniil Hayrapetyan, |
| Hayk Poghosyan, |
| Vahram Tadevosyan, |
| Zhangyang Wang, Shant Navasardyan, Humphrey Shi |
| </br> |
|
|
| [Video](https://www.youtube.com/watch?v=md4lp42vOGU) | [Project page](https://streamingt2v.github.io) | [Code](https://github.com/Picsart-AI-Research/StreamingT2V) |
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| <p align="center"> |
| <img src="__assets__/teaser/Streaming_SVD_teaser.jpg" width="800px"/> |
| <br> |
| <h2>🔥 Meet StreamingSVD - A StreamingT2V Method</h2> |
| <em> |
| StreamingSVD is an advanced autoregressive technique for image-to-video generation, generating long hiqh-quality videos with rich motion dynamics, turning SVD into a long video generator. Our method ensures temporal consistency throughout the video, aligns closely to the input image, and maintains high frame-level image quality. Our demonstrations include successful examples of videos up to 200 frames, spanning 8 seconds, and can be extended for even longer durations. |
| The effectiveness of the underlying autoregressive approach is not limited to the specific base model used, indicating that improvements in base models can yield even higher-quality videos. StreamingSVD is part of the StreamingT2V family. |
| </em> |
| </p> |
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| ## BibTeX |
| If you use our work in your research, please cite our publications: |
| ``` |
| StreamingSVD paper comming soon. |
| |
| @article{henschel2024streamingt2v, |
| title={StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text}, |
| author={Henschel, Roberto and Khachatryan, Levon and Hayrapetyan, Daniil and Poghosyan, Hayk and Tadevosyan, Vahram and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey}, |
| journal={arXiv preprint arXiv:2403.14773}, |
| year={2024} |
| } |
| ``` |
