2024 |
Lampe, Ajda; Stopar, Julija; Jain, Deepak Kumar; Omachi, Shinichiro; Peer, Peter; Struc, Vitomir DiCTI: Diffusion-based Clothing Designer via Text-guided Input Proceedings Article In: Proceedings of the18th International Conference on Automatic Face and Gesture Recognition (FG 2024), pp. 1-9, 2024. Abstract | Links | BibTeX | Tags: clothing design, deepbeauty, denoising diffusion probabilistic models, diffusion, diffusion models, fashion, virtual try-on @inproceedings{Ajda_Dicti,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. |
2022 |
Jug, Julijan; Lampe, Ajda; Peer, Peter; Štruc, Vitomir Segmentacija telesa z uporabo večciljnega učenja Proceedings Article In: Proceedings of Rosus 2022, 2022. Abstract | Links | BibTeX | Tags: deepbeauty, računalniški vid, segmentacija @inproceedings{Rosus2022,Segmentacija je pomemben del številnih problemov računalniškega vida, ki vključujejo človeške podobe, in je ena ključnih komponent, ki vpliva na uspešnost vseh nadaljnjih nalog. Več predhodnih del je ta problem obravnavalo z uporabo večciljnega modela, ki izkorišča korelacije med različnimi nalogami za izboljšanje uspešnosti segmentacije. Na podlagi uspešnosti takšnih rešitev v tem prispevku predstavljamo nov večciljni model za segmentacijo/razčlenjevanje ljudi, ki vključuje tri naloge, tj. (i) napoved skeletnih točk, (ii) napoved globinske predstavitve poze in (iii) segmentacijo človeškega telesa. Glavna ideja predlaganega modela Segmentacija-Skelet-Globinska predstavitev (ali na kratko iz angleščine SPD) je naučiti se boljšega modela segmentacije z izmenjavo znanja med različnimi, a med seboj povezanimi nalogami. SPD temelji na skupni hrbtenici globoke nevronske mreže, ki se razcepi na tri glave modela, specifične za nalogo, in se uči z uporabo cilja optimizacije za več nalog. Učinkovitost modela je analizirana s strogimi eksperimenti na nizih podatkov LIP in ATR ter v primerjavi z nedavnim (najsodobnejšim) večciljnim modelom segmentacije telesa. Predstavljene so tudi študije ablacije. Naši eksperimentalni rezultati kažejo, da je predlagani večciljni (segmentacijski) model zelo konkurenčen in da uvedba dodatnih nalog prispeva k večji skupni uspešnosti segmentacije. |
Fele, Benjamin; Lampe, Ajda; Peer, Peter; Štruc, Vitomir C-VTON: Context-Driven Image-Based Virtual Try-On Network Proceedings Article In: IEEE/CVF Winter Applications in Computer Vision (WACV), pp. 1–10, 2022. Abstract | Links | BibTeX | Tags: computer vision, deepbeauty, fashion, generative models, image editing, try-on, virtual try-on @inproceedings{WACV2022_Fele,Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art. |