Behind the Scenes of TryOnDiffusion

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.


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.

:::

Abstract and 1. Introduction

2. Related Work

3. Method

3.1. Cascaded Diffusion Models for Try-On

3.2. Parallel-UNet

4. Experiments

5. Summary and Future Work and References

\ Appendix

A. Implementation Details

B. Additional Results

3. Method

\

\

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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


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Backpropagation | Sciencx (2024-10-06T20:03:56+00:00) Behind the Scenes of TryOnDiffusion. Retrieved from https://www.scien.cx/2024/10/06/behind-the-scenes-of-tryondiffusion/

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