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[arXiv]
[bibtex]@InProceedings{Jain_2024_CVPR, author = {Jain, Yash and Nasery, Anshul and Vineet, Vibhav and Behl, Harkirat}, title = {PEEKABOO: Interactive Video Generation via Masked-Diffusion}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {8079-8088} }
PEEKABOO: Interactive Video Generation via Masked-Diffusion
Abstract
Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.
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