Create README.md

README.md

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+---
+pipeline_tag: image-to-video
+---
+
+
+# StreamingSVD
+
+
+**[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)
+
+
+<p align="center">
+<img src="__assets__/github/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>
+
+
+
+## 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}
+}
+```