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Abstract

Virtual try-on has attracted lots of research attention due to its potential applications in e-commerce, virtual reality and fashion design. However, existing methods can hardly preserve the finegrained details (e.g., clothing texture, facial identity, hair style, skin tone) during generation, due to the non-rigid body deformation and multi-scale details. In this work, we propose a multi-stage framework to synthesize person images, where fine-grained details can be well preserved. To address the long-range translation and rich-details generation, we propose a Tree-Block (tree dilated fusion block) to replace standard ResNet-block where applicable. Notably, multi-scale feature maps can be smoothly fused for finegrained detail generation, by incorporating larger spatial context at multiple scales. With a delicate end-to-end training scheme, our whole framework can be jointly optimized for results with significantly better visual fidelity and richer details. Moreover, we also explore the potential application in video-based virtual try-on. By harnessing the well-trained image generator and an extra videolevel adaptor, a model photo can be well animated with a driving pose sequence. Extensive evaluations on standard datasets and user study demonstrate that our proposed framework achieves the stateof-the-art results, especially in preserving visual details in clothing texture and facial identity. Our implementation is publicly available via this URL.

Enhanced Virtual Try on for Clothing using Deep learning

“…LITERATURE REVIEW Sangho Lee, Seoyoung Lee and Joonseok Lee (2022) [2] their research introduces the Clothes Fitting Module (CFM) [1] within the three-step Details-Preserving Virtual Try-On (DP-VTON) [1] model to effectively disentangle the characteristics of the person and source clothes. By employing VGG loss (LVGG) and L1 loss (L1) for training, DP-VTON significantly enhances the quality of virtual try-on experiences, outperforming the most up-to-date methods like CP-VTON+ [13], ACGPN [19], and PFAFN. DP-VTON [1] Expresses the target clothing's detailed characteristics accurately and faithfully, seamlessly fits various pose and body shape, addressing limitations observed in other approaches and advancing the field of virtual try-on technology CP-VTON [3] Wang et al presented Trademark Safeguarding Picture put together Virtual Attempt With respect to Organize (CPVTON) that can accomplish a persuading take a stab at picture unions.…”

Deep Learning in Virtual Try-On: A Comprehensive Survey

“…Most virtual try-on models have three major modules: segmentation, warping, and try-on synthesis [41], [49], [182]. The segmentation module is responsible for generating a semantic layout that aligns with the desired target pose.…”

“…This method gathers volunteers and asks them to choose the best images generated from different virtual try-on models based on specific criteria such as photorealism and accuracy. ACGPN [195], WUTON [85], PF-AFN [58], StylePoseGAN [159], HR-VTON [111], DCI-VTON [63], MG-VTON [41], FashionOn [77], TB-VTON [182], FW-GAN [42], MV-TON [207] and video attention-based method [176] all use user studies to demonstrate their superiority over their predecessors.…”

Survey on Virtual Dressing Room System Using Deep Neural Networks

“…The other they designed is a new U-Transformer, which is largely effective for generating largely-realistic images in a try-on emulsion. In [2], they investigated the virtual try-on under arbitrary acts has attracted lots of hunt attention due to its huge eventuality operations. However, being styles can hardly save the details in clothing texture and facial identity( face, hair) while fitting new clothes and acts onto a person.…”