DiCTI: Diffusion-based Clothing Designer via Text-guided Input

TL;DR

DiCTI introduces a diffusion-based method enabling rapid, high-quality, text-guided clothing design visualization, significantly improving virtual fashion prototyping for designers and customers.

Contribution

It presents a novel diffusion-based inpainting model conditioned on text, allowing quick, realistic clothing image generation from text descriptions in unconstrained settings.

Findings

Outperforms state-of-the-art in image quality and garment complexity

Achieves better adherence to text prompts in generated images

Receives higher human ratings in user studies

Abstract

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study.