This content originally appeared on HackerNoon and was authored by Backpropagation
:::info Authors:
(1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google;
(2) Dawei Yang, Google Research;
(3) Tyler Zhu, Google Research;
(4) Fitsum Reda, Google Research;
(5) William Chan, Google Research;
(6) Chitwan Saharia, Google Research;
(7) Mohammad Norouzi, Google Research;
(8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research.
:::
Table of Links
3.1. Cascaded Diffusion Models for Try-On
5. Summary and Future Work and References
\ Appendix
5. Summary and Future Work
We presented a method that allows to synthesize try-on given an image of a person and an image of a garment. Our results are overwhelmingly better than state-of-the-art, both in the quality of the warp to new body shapes and poses, and in the preservation of the garment. Our novel architecture Parallel-UNet, where two UNets are trained in parallel and one UNet sends information to the other via cross attentions, turned out to create state-of-the-art results. In addition to the exciting progress for the specific application of virtual try-on, we believe this architecture is going to be impactful for the general case of image editing, which we are excited to explore in the future. Finally, we believe that the architecture could also be extended to videos, which we also plan to pursue in the future.
References
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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Backpropagation
Backpropagation | Sciencx (2024-10-06T20:04:27+00:00) Revolutionizing Virtual Try-On: Key Findings and Future Directions. Retrieved from https://www.scien.cx/2024/10/06/revolutionizing-virtual-try-on-key-findings-and-future-directions/
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