TryOffDiff

TryOffDiff is a high-fidelity clothing reconstruction technology based on diffusion models used to generate standardized clothing images from single photos of individuals wearing them. This technology is different from traditional virtual try-ons, which aims to extract standardized garment images, which presents unique challenges in capturing garment shapes, textures, and complex patterns. TryOffDiff ensures high fidelity and detail retention by using Stable Diffusion and SigLIP-based visual conditions. Experiments of this technology on the VITON-HD dataset show that its method outperforms the baseline method based on pose transfer and virtual trial-on and requires fewer pre- and post-processing steps. TryOffDiff can not only improve the quality of e-commerce product images, but also promote the evaluation of generative models and stimulate future work in high-fidelity reconstruction.

Demand population:

"The target audience includes e-commerce platforms, clothing retailers, fashion designers, and researchers in the field of image processing. TryOffDiff can help them improve product display, optimize customer experience through high-fidelity clothing image reconstruction technology, and achieve more accurate clothing image analysis in design and research."

Example of usage scenarios:

E-commerce websites use TryOffDiff to display clothing products to improve the online shopping experience.

Clothing designers use TryOffDiff technology to digitally display clothing design.

Image processing researchers use TryOffDiff for research and development of high-fidelity clothing image reconstruction.

Product Features:

- High-fidelity clothing image reconstruction: Extracting normative images of clothing from single photos.

- Detail retention: Ensure the shape, texture and complex patterns of the garment are accurately captured.

- Based on diffusion model: Use Stable Diffusion technology to generate clothing images.

- SigLIP Visual Conditions: Improve the accuracy of clothing reconstruction through visual conditions.

- Reduce pre-processing and post-processing steps: simplifies the conversion process from original images to standardized clothing images.

- Improve image quality of e-commerce products: suitable for product display in online retail environments.

- Advance generative model evaluation: provides a new approach to assessing the reconstruction fidelity of generative models.

- Inspire high-fidelity reconstruction research: providing new directions for future research in the field of clothing image reconstruction.

Tutorials for use:

1. Visit TryOffDiff 's official website or demo page.

2. Upload a photo of an individual wearing a costume.

3. Select the TryOffDiff model for reconstructing the clothing image.

4. Adjust the visual condition parameters as needed to obtain the best clothing image reconstruction effect.

5. Download or view the high-fidelity clothing reconstruction results directly on the website.

6. Apply reconstructed clothing images to e-commerce product display or design work.

7. Adjust reconstruction parameters based on feedback to optimize the quality and details of the clothing image.