Toward Characteristic-Preserving Image-based Virtual Try-On Network (1807.07688v3)

Abstract: Image-based virtual try-on systems for fitting new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. A desirable pipeline should not only transform the target clothes into the most fitting shape seamlessly but also preserve well the clothes identity in the generated image, that is, the key characteristics (e.g. texture, logo, embroidery) that depict the original clothes. However, previous image-conditioned generation works fail to meet these critical requirements towards the plausible virtual try-on performance since they fail to handle large spatial misalignment between the input image and target clothes. Prior work explicitly tackled spatial deformation using shape context matching, but failed to preserve clothing details due to its coarse-to-fine strategy. In this work, we propose a new fully-learnable Characteristic-Preserving Virtual Try-On Network(CP-VTON) for addressing all real-world challenges in this task. First, CP-VTON learns a thin-plate spline transformation for transforming the in-shop clothes into fitting the body shape of the target person via a new Geometric Matching Module (GMM) rather than computing correspondences of interest points as prior works did. Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness. Extensive experiments on a fashion dataset demonstrate our CP-VTON achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.

• The paper introduces CP-VTON, a two-stage network that uses a fully learnable geometric matching module to address spatial misalignments in virtual try-on tasks.
• It employs a try-on module with dynamic blending using L1 and perceptual losses to preserve detailed textures and logos in apparel.
• Extensive experiments show that CP-VTON outperforms prior methods like VITON, promising enhanced realism in virtual try-on applications.

Toward Characteristic-Preserving Image-based Virtual Try-On Network

The paper entitled "Toward Characteristic-Preserving Image-based Virtual Try-On Network" introduces a novel approach, CP-VTON, to tackle the challenges inherent in virtual try-on systems, specifically focusing on the preservation of key characteristics of the clothing items while ensuring seamless integration with the target person image. Virtual try-on systems have gained traction in recent years due to their potential to enhance online shopping experiences by allowing users to visualize themselves in different apparel without the need for physical trials. The authors identify several limitations in existing methods, notably their inability to manage significant spatial misalignments between the clothing item and the target body shape while maintaining critical details such as texture and logos.

Methodological Contributions

Key to the success of CP-VTON is its novel architecture, which comprises two primary modules:

1. Geometric Matching Module (GMM): This module addresses the spatial misalignment challenge by employing a thin-plate spline transformation to warp the in-shop clothing item to fit the target person's body shape. Unlike prior methods that relied on point correspondences and were susceptible to errors from inaccurate mask predictions, the GMM uses a fully learnable framework. It captures the spatial transformation requirements through a Convolutional Neural Network (CNN), trained in a supervised manner utilizing pixel-wise L1 losses.
2. Try-On Module: After alignment, the Try-On Module generates the final images by fusing the warped clothing with a rendered version of the person image. A composition mask is utilized to dynamically blend the two inputs, ensuring both seamless integration and the preservation of clothing characteristics. The blending process is guided by a combination of L1 and perceptual losses, with the latter ensuring high-level feature alignment with the ground truth.

Experimental Validation

The authors validate their approach rigorously using a dataset collected by Han et al., performing both qualitative and quantitative evaluations. CP-VTON is demonstrated to outperform existing methods, notably VITON, by achieving superior preservation of clothing details while maintaining visual realism. Quantitative assessments were conducted via pairwise human preference studies, indicating a preference for CP-VTON, especially on challenging inputs with detailed textures.

Implications and Future Directions

This research has several implications for the development of virtual try-on technology in practical applications. The ability to preserve detailed characteristics of clothes while ensuring a realistic integration into user images is crucial for their adoption in e-commerce platforms. Moreover, the method's reliance on a two-stage pipeline addressing alignment and synthesis separately suggests a potential for further modulation, unconstrained by the limitations of previous single-stage methods.

For future research, enhancements could be directed toward improving the robustness of the system against edge cases, such as those reflected in rare poses or ambiguous clothing silhouettes. Additionally, expanding the application beyond women's apparel to include a wider range of garment types and multi-view synthesis could enhance the versatility and appeal of the approach.

In conclusion, "Toward Characteristic-Preserving Image-based Virtual Try-On Network" presents a significant advancement in the field of virtual try-on systems. It successfully combines learnable geometric transformation with sophisticated image synthesis techniques, marking a step forward in achieving high fidelity in virtual apparel trials. The code being publicly available provides an opportunity for further development and integration into commercial systems.