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Toward Accurate, Realistic Virtual Try-on Through Shape Matching: Conclusions & Referencesby@polyframe
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Toward Accurate, Realistic Virtual Try-on Through Shape Matching: Conclusions & References

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Researchers improve virtual try-on methods by using a new dataset to choose target models and train specialized warpers, enhancing realism and accuracy.
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Authors:

(1) Kedan Li, University of Illinois at Urbana-Champaign;

(2) Min Jin Chong, University of Illinois at Urbana-Champaign;

(3) Jingen Liu, JD AI Research;

(4) David Forsyth, University of Illinois at Urbana-Champaign.

5. Conclusions

In this paper, we propose two general modifications to the virtual try-on framework: (a) carefully choose the product-model pair for transfer using a shape embedding and (b) combine multiple coordinated warps using inpainting. Our results show that both modifications lead to significant improvement in generation quality. Qualitative examples demonstrate our ability to accurately preserve details of garments. This lead to difficulties for shoppers to distinguish between real and synthesized model images, shown by user study results.

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