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@inproceedings{elmar2026,

title = {CloDe: A Multimodal Tuning-Free Clothing Designer},

author = {Ajda Lampe and Ana Cvetković and Dong Seog Han and Satish Kumar Singh and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2026/09/ELMAR_2026___Anac.pdf},

year  = {2026},

date = {2026-08-25},

booktitle = {Proceedings of ELMAR 2026},

pages = {1--4},

abstract = {Recent diffusion-based image generation models have enabled new directions in virtual try-on and garment synthesis. However, existing approaches often trade off between controllability and accessibility: tuning-based methods typically require additional training or specialized inputs, while tuning-free methods may lack structural control and fine-grained appearance guidance. In this work, we propose CloDe, a multimodal, tuning-free garment synthesis framework built on a pretrained diffusion inpainting model. Our method enhances controllability without finetuning the backbone by introducing three components: (i) a category-aware masking strategy that improves edit locality and reduces garment leakage, (ii) pose conditioning via ControlNet to better preserve human structure, and (iii) optional fabric conditioning using IP-Adapter to incorporate fine-grained appearance cues. Built on DiCTI, our approach maintains a fully zero-shot pipeline while improving synthesis quality across realism, pose consistency, and texture fidelity. We evaluate CloDe on VITON-HD using quantitative metrics and a user study, showing consistent improvements over DiCTI in both objective measures and perceptual quality.},

keywords = {CloDE, clothing design, deepbeauty, diffusion models, virtual design, virtual try-on},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ajda_Dicti,

title = {DiCTI: Diffusion-based Clothing Designer via Text-guided Input},

author = {Ajda Lampe and Julija Stopar and Deepak Kumar Jain and Shinichiro Omachi and Peter Peer and Vitomir Struc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/06/Dicti_FG2024_compressed.pdf},

year  = {2024},

date = {2024-05-27},

booktitle = {Proceedings of the18th International Conference on Automatic Face and Gesture Recognition (FG 2024)},

pages = {1-9},

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. The source code of DiCTI will be made publicly available.},

keywords = {clothing design, deepbeauty, denoising diffusion probabilistic models, diffusion, diffusion models, fashion, virtual try-on},

pubstate = {published},

tppubtype = {inproceedings}

}