What is it about?

DMHomo is a framework designed to convert unsupervised data into supervised data, which can help address the issue of data scarcity. By using only a 3x3 homography matrix and a mask, it is possible to obtain two consecutive video frames that adhere to the matrix relationship through the diffusion model.

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Why is it important?

An unlabeled dataset can be converted into an accurate supervised dataset, and this approach may be applied in the field of video generation.


I feel very honored to be able to publish a paper in ToG, and I hope my work can contribute to AI 2.0.

Haipeng Li
University of Electronic Science and Technology of China

Read the Original

This page is a summary of: DMHomo: Learning Homography with Diffusion Models, ACM Transactions on Graphics, March 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3652207.
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