Summary of Dh-vton: Deep Text-driven Virtual Try-on Via Hybrid Attention Learning, by Jiabao Wei and Zhiyuan Ma
DH-VTON: Deep Text-Driven Virtual Try-On via Hybrid Attention Learning
by Jiabao Wei, Zhiyuan Ma
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Virtual Try-On (VTON) paper proposes a deep text-driven virtual try-on model called DH-VTON to synthesize specific person images dressed in given garments. The core challenge is extracting fine-grained semantics from reference garments during depth estimation and preserving texture when synthesizing on human bodies. To address this, the authors introduce a hybrid attention learning strategy and deep garment semantic preservation module, building upon a well-trained paint-by-example (PBE) approach. Specifically, they use InternViT-6B as a fine-grained feature learner to align with large-scale intrinsic knowledge and deep text semantics. The Garment-Feature ControlNet Plus (GFC+) module is introduced to enhance customized dressing abilities, integrating fine-grained garment characteristics into the VTON model’s different layers. Experimental results on representative datasets show that DH-VTON outperforms previous diffusion-based and GAN-based approaches in preserving garment details and generating authentic human images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying on clothes online without actually putting them on! This paper tries to make that happen by creating a computer model called Virtual Try-On (VTON). The challenge is to get the model to understand what’s on the clothes, like stripes or patterns, and then put those clothes on someone else. To do this, the authors use a special combination of techniques to help the model learn about different parts of the clothes and how they look when worn by people. They tested their model on lots of pictures and it did better than other similar models at making realistic and detailed images. |
Keywords
» Artificial intelligence » Attention » Depth estimation » Diffusion » Gan » Semantics
