Summary of Dicti: Diffusion-based Clothing Designer Via Text-guided Input, by Ajda Lampe (2) et al.
DiCTI: Diffusion-based Clothing Designer via Text-guided Input
by Ajda Lampe, Julija Stopar, Deepak Kumar Jain, Shinichiro Omachi, Peter Peer, Vitomir Štruc
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Recent advances in deep generative models have revolutionized image synthesis, transforming various creative industries like fashion. While numerous methods focus on virtual try-on for buyers, there’s been limited attention to facilitating fast prototyping for designers and customers seeking new designs. To bridge this gap, we introduce DiCTI, a straightforward yet highly effective approach that enables designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of desired garments as input, DiCTI generates multiple high-resolution, photorealistic images capturing the expressed semantics. Leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI synthesizes convincing, high-quality images with varied clothing designs that viably follow provided text descriptions. We evaluate DiCTI in comprehensive experiments on two datasets (VITON-HD and Fashionpedia) and compare it to the state-of-the-art (SoTa). Results show that DiCTI convincingly outperforms SoTa in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to design new clothes just by typing what you want! That’s the idea behind a new technology called DiCTI. It helps designers quickly create pictures of people wearing different outfits based on what they write. Right now, this is mostly used for virtual try-on in stores, but it can also help customers and designers work together to create new fashion ideas. DiCTI uses special computer algorithms that take an image of a person and a description of the clothes they want, then generate multiple high-quality pictures that match what was written. This technology has been tested on two different sets of images and compared to other state-of-the-art methods. The results show that DiCTI is better at generating high-quality images with more detailed clothing designs that match the text descriptions. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image synthesis » Prompt » Semantics
