Improving Virtual Try-On with Garment-focused Diffusion Models

Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for synthesizing an image of a target person wearing a given in-shop garment, i.e., image-based virtual try-on (VTON) task. The difficulty originates from the aspect that the diffusion process should not only produce holistically high-fidelity photorealistic image of the target person, but also locally preserve every appearance and texture detail of the given garment. To address this, we shape a new Diffusion model, namely GarDiff, which triggers the garment-focused diffusion process with amplified guidance of both basic visual appearance and detailed textures (i.e., high-frequency details) derived from the given garment. GarDiff first remoulds a pre-trained latent diffusion model with additional appearance priors derived from the CLIP and VAE encodings of the reference garment. Meanwhile, a novel garment-focused adapter is integrated into the UNet of diffusion model, pursuing local fine-grained alignment with the visual appearance of reference garment and human pose. We specifically design an appearance loss over the synthesized garment to enhance the crucial, high-frequency details. Extensive experiments on VITON-HD and DressCode datasets demonstrate the superiority of our GarDiff when compared to state-of-the-art VTON approaches. Code is publicly available at: \href{https://github.com/siqi0905/GarDiff/tree/master}{https://github.com/siqi0905/GarDiff/tree/master}.

VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding

Although diffusion-based image virtual try-on has made considerable progress, emerging approaches still struggle to effectively address the issue of hand occlusion (i.e., clothing regions occluded by the hand part), leading to a notable degradation of the try-on performance. To tackle this issue widely existing in real-world scenarios, we propose VTON-HandFit, leveraging the power of hand priors to reconstruct the appearance and structure for hand occlusion cases. Firstly, we tailor a Handpose Aggregation Net using the ControlNet-based structure explicitly and adaptively encoding the global hand and pose priors. Besides, to fully exploit the hand-related structure and appearance information, we propose Hand-feature Disentanglement Embedding module to disentangle the hand priors into the hand structure-parametric and visual-appearance features, and customize a masked cross attention for further decoupled feature embedding. Lastly, we customize a hand-canny constraint loss to better learn the structure edge knowledge from the hand template of model image. VTON-HandFit outperforms the baselines in qualitative and quantitative evaluations on the public dataset and our self-collected hand-occlusion Handfit-3K dataset particularly for the arbitrary hand pose occlusion cases in real-world scenarios. The Code and dataset will be available at \url{https://github.com/VTON-HandFit/VTON-HandFit}.

BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training

Image-based virtual try-on is an increasingly popular and important task to generate realistic try-on images of specific person. Existing methods always employ an accurate mask to remove the original garment in the source image, thus achieving realistic synthesized images in simple and conventional try-on scenarios based on powerful diffusion model. Therefore, acquiring suitable mask is vital to the try-on performance of these methods. However, obtaining precise inpainting masks, especially for complex wild try-on data containing diverse foreground occlusions and person poses, is not easy as Figure 1-Top shows. This difficulty often results in poor performance in more practical and challenging real-life scenarios, such as the selfie scene shown in Figure 1-Bottom. To this end, we propose a novel training paradigm combined with an efficient data augmentation method to acquire large-scale unpaired training data from wild scenarios, thereby significantly facilitating the try-on performance of our model without the need for additional inpainting masks. Besides, a try-on localization loss is designed to localize a more accurate try-on area to obtain more reasonable try-on results. It is noted that our method only needs the reference cloth image, source pose image and source person image as input, which is more cost-effective and user-friendly compared to existing methods. Extensive qualitative and quantitative experiments have demonstrated superior performance in wild scenarios with such a low-demand input.

SiCo: A Size-Controllable Virtual Try-On Approach for Informed Decision-Making

Virtual try-on (VTO) applications aim to improve the online shopping experience by allowing users to preview garments, before making purchase decisions. However, many VTO tools fail to consider the crucial relationship between a garment's size and the user's body size, often employing a one-size-fits-all approach when visualizing a clothing item. This results in poor size recommendations and purchase decisions leading to increased return rates. To address this limitation, we introduce SiCo, an online VTO system, where users can upload images of themselves and visualize how different sizes of clothing would look on their body to help make better-informed purchase decisions. Our user study shows SiCo's superiority over baseline VTO. The results indicate that our approach significantly enhances user ability to gauge the appearance of outfits on their bodies and boosts their confidence in selecting clothing sizes that match desired goals. Based on our evaluation, we believe our VTO design has the potential to reduce return rates and enhance the online clothes shopping experience. Our code is available at https://github.com/SherryXTChen/SiCo.

Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering

With the support of Virtual Reality (VR) and Augmented Reality (AR) technologies, the 3D virtual eyeglasses try-on application is well on its way to becoming a new trending solution that offers a "try on" option to select the perfect pair of eyeglasses at the comfort of your own home. Reconstructing eyeglasses frames from a single image with traditional depth and image-based methods is extremely difficult due to their unique characteristics such as lack of sufficient texture features, thin elements, and severe self-occlusions. In this paper, we propose the first mesh deformation-based reconstruction framework for recovering high-precision 3D full-frame eyeglasses models from a single RGB image, leveraging prior and domain-specific knowledge. Specifically, based on the construction of a synthetic eyeglasses frame dataset, we first define a class-specific eyeglasses frame template with pre-defined keypoints. Then, given an input eyeglasses frame image with thin structure and few texture features, we design a keypoint detector and refiner to detect predefined keypoints in a coarse-to-fine manner to estimate the camera pose accurately. After that, using differentiable rendering, we propose a novel optimization approach for producing correct geometry by progressively performing free-form deformation (FFD) on the template mesh. We define a series of loss functions to enforce consistency between the rendered result and the corresponding RGB input, utilizing constraints from inherent structure, silhouettes, keypoints, per-pixel shading information, and so on. Experimental results on both the synthetic dataset and real images demonstrate the effectiveness of the proposed algorithm.

OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person

Virtual Try-On (VTON) has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent results. While diffusion models, such as Stable Diffusion series, have shown their capability in creating high-quality and photorealistic images, they encounter formidable challenges in conditional generation scenarios like VTON. Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials. OutfitAnyone addresses these limitations by leveraging a two-stream conditional diffusion model, enabling it to adeptly handle garment deformation for more lifelike results. It distinguishes itself with scalability-modulating factors such as pose, body shape and broad applicability, extending from anime to in-the-wild images. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment. For more details and animated results, please see \url{https://humanaigc.github.io/outfit-anyone/}.

DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models

Image-based 3D Virtual Try-ON (VTON) aims to sculpt the 3D human according to person and clothes images, which is data-efficient (i.e., getting rid of expensive 3D data) but challenging. Recent text-to-3D methods achieve remarkable improvement in high-fidelity 3D human generation, demonstrating its potential for 3D virtual try-on. Inspired by the impressive success of personalized diffusion models (e.g., Dreambooth and LoRA) for 2D VTON, it is straightforward to achieve 3D VTON by integrating the personalization technique into the diffusion-based text-to-3D framework. However, employing the personalized module in a pre-trained diffusion model (e.g., StableDiffusion (SD)) would degrade the model's capability for multi-view or multi-domain synthesis, which is detrimental to the geometry and texture optimization guided by Score Distillation Sampling (SDS) loss. In this work, we propose a novel customizing 3D human try-on model, named \textbf{DreamVTON}, to separately optimize the geometry and texture of the 3D human. Specifically, a personalized SD with multi-concept LoRA is proposed to provide the generative prior about the specific person and clothes, while a Densepose-guided ControlNet is exploited to guarantee consistent prior about body pose across various camera views. Besides, to avoid the inconsistent multi-view priors from the personalized SD dominating the optimization, DreamVTON introduces a template-based optimization mechanism, which employs mask templates for geometry shape learning and normal/RGB templates for geometry/texture details learning. Furthermore, for the geometry optimization phase, DreamVTON integrates a normal-style LoRA into personalized SD to enhance normal map generative prior, facilitating smooth geometry modeling.

CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models

Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs. In this work, we rethink the necessity of ReferenceNet and image encoders and innovate the interaction between garment and person by proposing CatVTON, a simple and efficient virtual try-on diffusion model. CatVTON facilitates the seamless transfer of in-shop or worn garments of any category to target persons by simply concatenating them in spatial dimensions as inputs. The efficiency of our model is demonstrated in three aspects: (1) Lightweight network: Only the original diffusion modules are used, without additional network modules. The text encoder and cross-attentions for text injection in the backbone are removed, reducing the parameters by 167.02M. (2) Parameter-efficient training: We identified the try-on relevant modules through experiments and achieved high-quality try-on effects by training only 49.57M parameters, approximately 5.51 percent of the backbone network's parameters. (3) Simplified inference: CatVTON eliminates all unnecessary conditions and preprocessing steps, including pose estimation, human parsing, and text input, requiring only a garment reference, target person image, and mask for the virtual try-on process. Extensive experiments demonstrate that CatVTON achieves superior qualitative and quantitative results with fewer prerequisites and trainable parameters than baseline methods. Furthermore, CatVTON shows good generalization in in-the-wild scenarios despite using open-source datasets with only 73K samples.

D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On

In this paper, we introduce D$^4$-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping, and reliance on static, annotation-driven clothing parsers. Additionally, we tackle the complexities in diffusion-based VTON models when handling simultaneous tasks like inpainting and denoising. Our approach utilizes two key technologies: Firstly, Dynamic Semantics Disentangling Modules (DSDMs) extract abstract semantic information from garments to create distinct local flows, improving precise garment warping in a self-discovered manner. Secondly, by integrating a Differential Information Tracking Path (DITP), we establish a novel diffusion-based VTON paradigm. This path captures differential information between incomplete try-on inputs and their complete versions, enabling the network to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that D$^4$-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrating its capability in generating realistic images and ensuring semantic consistency.

WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models

Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions, limiting their effectiveness in video try-on applications. Moreover, video-based models require extensive, high-quality data and substantial computational resources. To tackle these issues, we reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach. This model, conditioned on specific garments and individuals, is trained on still images rather than videos. It leverages diffusion guidance from pre-trained models including a video masked autoencoder for segment smoothness improvement and a self-supervised model for feature alignment of adjacent frame in the latent space. This integration markedly boosts the model's ability to maintain temporal coherence, enabling more effective video try-on within an image-based framework. Our experiments on the VITON-HD and DressCode datasets, along with tests on the VVT and TikTok datasets, demonstrate WildVidFit's capability to generate fluid and coherent videos. The project page website is at wildvidfit-project.github.io.