TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models (2411.18350v1)

Abstract: This paper introduces Virtual Try-Off (VTOFF), a novel task focused on generating standardized garment images from single photos of clothed individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses models, VTOFF aims to extract a canonical garment image, posing unique challenges in capturing garment shape, texture, and intricate patterns. This well-defined target makes VTOFF particularly effective for evaluating reconstruction fidelity in generative models. We present TryOffDiff, a model that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure high fidelity and detail retention. Experiments on a modified VITON-HD dataset show that our approach outperforms baseline methods based on pose transfer and virtual try-on with fewer pre- and post-processing steps. Our analysis reveals that traditional image generation metrics inadequately assess reconstruction quality, prompting us to rely on DISTS for more accurate evaluation. Our results highlight the potential of VTOFF to enhance product imagery in e-commerce applications, advance generative model evaluation, and inspire future work on high-fidelity reconstruction. Demo, code, and models are available at: https://rizavelioglu.github.io/tryoffdiff/

• The paper introduces the VTOFF task, which extracts canonical garment images from single photos, capturing shape, texture, and intricate design details.
• The paper proposes TryOffDiff, leveraging Stable Diffusion with enhanced SigLIP conditioning to achieve high-fidelity garment image generation.
• The paper demonstrates TryOffDiff’s superior efficiency and quality, validated on a modified VITON-HD dataset using the perceptual DISTS metric.

Overview of "TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models"

This paper presents a methodological advancement in the domain of computer vision, specifically focusing on garment image generation. The authors introduce the Virtual Try-Off (VTOFF) task, a novel approach aimed at reconstructing canonical garment images from single photos of individuals wearing the clothing. Differing fundamentally from the more traditional Virtual Try-On (VTON) that creates composite images of people with specified garments, VTOFF concentrates on accurately capturing garment shape, texture, and intricate design elements from a limited visual input.

Key Contributions

1. Introduction of VTOFF: The VTOFF task is defined as a specific challenge, setting itself apart from VTON by targeting the extraction of garments from images in a standardized and consistent manner. This task highlights the difficulty in detail retention when capturing complex patterns and shapes from occluded garment images on individuals.
2. TryOffDiff Modeling: The paper introduces TryOffDiff, a model that leverages the Stable Diffusion architecture with enhanced SigLIP-based visual conditioning. The model focuses on maintaining high fidelity and detail retention, which is paramount for generating high-quality garment images suitable for e-commerce.
3. Empirical Evaluation: Extensive experiments conducted on a modified VITON-HD dataset demonstrate that TryOffDiff outperforms traditional baseline methods rooted in pose transfer and virtual try-on. Significantly, it achieves higher efficiency with reduced dependencies on extensive processing.

Methodological Approaches

The core innovation resides in its exploitation of diffusion models for garment image generation tasks. The authors demonstrate that traditional image quality metrics like SSIM are insufficient for accurate evaluation of generative tasks, promoting the use of the DISTS metric which more accurately reflects perceptual quality as perceived by humans. By building on pretrained models and adapting visual conditioning layers, TryOffDiff efficiently creates high-fidelity garment reconstructions.

Implications and Future Directions

Practically, VTOFF holds substantial implications for the e-commerce sector, promising enhanced methods for product image creation, which are critical for consumer engagement in digital platforms. Theoretically, this task allows for deeper exploration into high-fidelity generative model evaluations, potentially guiding future research in computer vision and AI by setting benchmarks for gauging manufacture-standard outputs. Furthermore, this work could catalyze new fashion dataset creation, promoting advancements in AI auctioning without significant resource allocation toward extensive physical photo shoots.

Conclusion

"TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models" introduces an important distinction in image generation tasks, effectively expanding the capabilities of generative models to perform highly detailed garment extractions in the virtual retail environment. The paper lays a substantial groundwork for forthcoming explorations into further optimizing model conditioning techniques and reconstruction metrics tailored for specific industry needs. With its immediate practical applications and theoretical advancements, this paper stands as a significant contribution to the future of AI-driven apparel and e-commerce solutions.