paint-brush
How Parallel-UNet Transforms Virtual Try-On with Implicit Warping and Unified Operationsby@backpropagation
106 reads

How Parallel-UNet Transforms Virtual Try-On with Implicit Warping and Unified Operations

by Backpropagation
Backpropagation HackerNoon profile picture

Backpropagation

@backpropagation

Uncovering hidden patterns with backpropagation, a powerful but often misunderstood...

October 6th, 2024
Read on Terminal Reader
Read this story in a terminal
Print this story
Read this story w/o Javascript
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

The 128×128 Parallel-UNet employs implicit warping using a cross attention mechanism to manage complex transformations like garment warping. By combining warping and blending into a single pass, the architecture utilizes two UNets to efficiently process person and garment images. Pose embeddings guide these operations, enhancing the correspondence between the target person and the garment.
featured image - How Parallel-UNet Transforms Virtual Try-On with Implicit Warping and Unified Operations
1x
Read by Dr. One voice-avatar

Listen to this story

Backpropagation HackerNoon profile picture
Backpropagation

Backpropagation

@backpropagation

Uncovering hidden patterns with backpropagation, a powerful but often misunderstood algorithm shaping AI insights.

Learn More
LEARN MORE ABOUT @BACKPROPAGATION'S
EXPERTISE AND PLACE ON THE INTERNET.
0-item

STORY’S CREDIBILITY

Academic Research Paper

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.

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

3.2. Parallel-UNet

The 128×128 Parallel-UNet can be represented as


image


image


Table 1. Quantitative comparison to 3 baselines. We compute FID and KID on our 6K test set and VITON-HD’s unpaired test set. The KID is scaled by 1000 following [22].

Table 1. Quantitative comparison to 3 baselines. We compute FID and KID on our 6K test set and VITON-HD’s unpaired test set. The KID is scaled by 1000 following [22].


Combining warp and blend in a single pass. Instead of warping the garment to the target body and then blending with the target person as done by prior works, we combine the two operations into a single pass. As shown in Fig. 2, we achieve it via two UNets that handle the garment and the person respectively.


image


This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.


L O A D I N G
. . . comments & more!

About Author

Backpropagation HackerNoon profile picture
Backpropagation@backpropagation
Uncovering hidden patterns with backpropagation, a powerful but often misunderstood algorithm shaping AI insights.

TOPICS

THIS ARTICLE WAS FEATURED IN...

Permanent on Arweave
Read on Terminal Reader
Read this story in a terminal
 Terminal
Read this story w/o Javascript
Read this story w/o Javascript
 Lite
Also published here
X
X REMOVE AD