Software & Data Downloads — TI2V-Zero

Zero-Shot Image Conditioning for Text-to-Video Diffusion Models for empowering a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules.

This is the code for the CVPR 2024 publication TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models. It allows users to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is drinking water") based on a pretrained text-to-video (T2V) diffusion model, without any additional training or fine-tuning.

  •  Ni, H., Egger, B., Lohit, S., Cherian, A., Wang, Y., Koike-Akino, T., Huang, S.X., Marks, T.K., "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2024, pp. 9015-9025.
    BibTeX TR2024-059 PDF Video Software Presentation
    • @inproceedings{Ni2024jun,
    • author = {Ni, Haomiao and Egger, Bernhard and Lohit, Suhas and Cherian, Anoop and Wang, Ye and Koike-Akino, Toshiaki and Huang, Sharon X. and Marks, Tim K.},
    • title = {TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models},
    • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    • year = 2024,
    • pages = {9015--9025},
    • month = jun,
    • url = {https://www.merl.com/publications/TR2024-059}
    • }

Access software at https://github.com/merlresearch/TI2V-Zero.