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.
Featured Image
Photo by Jp Valery on Unsplash
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.
Perspectives
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.
You can read the full text:
Contributors
The following have contributed to this page
