Behind the Scenes of TryOnDiffusion
by Backpropagation1 min readOctober 6th, 2024
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Uncovering hidden patterns with backpropagation, a powerful but often misunderstood...
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The proposed method for virtual try-on utilizes images of a person and a garment worn by another person to synthesize a realistic visualization. The process begins with preprocessing, which includes predicting human parsing maps and 2D pose keypoints. The images are transformed into clothing-agnostic representations, ensuring minimal leakage of garment information. Additionally, the use of diffusion models facilitates an iterative denoising process for generating high-quality outputs conditioned on various inputs.
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Backpropagation
@backpropagation
Uncovering hidden patterns with backpropagation, a powerful but often misunderstood algorithm shaping AI insights.
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Academic Research Paper
Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.
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
3. Method
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
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