GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting
(2405.07472)Abstract
The increasing prominence of e-commerce has underscored the importance of Virtual Try-On (VTON). However, previous studies predominantly focus on the 2D realm and rely heavily on extensive data for training. Research on 3D VTON primarily centers on garment-body shape compatibility, a topic extensively covered in 2D VTON. Thanks to advances in 3D scene editing, a 2D diffusion model has now been adapted for 3D editing via multi-viewpoint editing. In this work, we propose GaussianVTON, an innovative 3D VTON pipeline integrating Gaussian Splatting (GS) editing with 2D VTON. To facilitate a seamless transition from 2D to 3D VTON, we propose, for the first time, the use of only images as editing prompts for 3D editing. To further address issues, e.g., face blurring, garment inaccuracy, and degraded viewpoint quality during editing, we devise a three-stage refinement strategy to gradually mitigate potential issues. Furthermore, we introduce a new editing strategy termed Edit Recall Reconstruction (ERR) to tackle the limitations of previous editing strategies in leading to complex geometric changes. Our comprehensive experiments demonstrate the superiority of GaussianVTON, offering a novel perspective on 3D VTON while also establishing a novel starting point for image-prompting 3D scene editing.
Overview
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GaussianVTON integrates Gaussian Splatting and advanced image editing to enhance 3D Virtual Try-On (VTON), blending the precision of 2D systems with complexity management in three-dimensional environments.
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The framework features a three-stage refinement strategy for virtual try-ons, which includes facial optimization, hierarchical sparse editing, and quality enhancement, along with using image prompts for more accurate depiction of clothing preferences.
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The technology's applications range from improving online shopping experiences to assisting fashion design and creating content for entertainment and social media, with potential future extensions into AR/VR and AI-driven personalization.
Exploring GaussianVTON: A Novel Approach for 3D Virtual Try-On
Introduction to GaussianVTON
Virtual Try-On (VTON) technologies have become increasingly relevant with the rise of e-commerce, providing a digital solution for shoppers to visualize themselves in clothing without the need for physical fitting. The paper introduces GaussianVTON, a novel framework that elevates 3D VTON by integrating Gaussian Splatting with advanced image editing techniques. This approach leverages the strengths of 2D VTON while addressing the complexities associated with transitioning these methods into three-dimensional spaces.
Key Innovations
The research presents several significant contributions and innovations:
- 3D Scene Editing with Image Prompts: Unlike traditional methods that primarily utilize text prompts for scene edits, GaussianVTON uniquely uses image prompts. This innovation better captures the nuances of users' clothing preferences, translating them more accurately into the virtual environment.
- Three-Stage Refinement Strategy: This strategy is a structured approach to refine the try-on process, involving:
- Facial Optimization: Enhances facial details which often get blurred in traditional 2D and 3D VTON processes.
- Hierarchical Sparse Editing: Addresses inconsistencies across different views by editing specific areas that are not aligning well.
- Quality Enhancement: Improves the overall visual quality of the generated images, ensuring that the clothing fits seamlessly onto the virtual models.
- Edit Recall Reconstruction (ERR): A sophisticated editing technique that handles complex geometric changes more effectively than previous methods. This strategy avoids common pitfalls like object misplacements or unwanted edits in unspecified regions.
Practical Uses and Implications
The practical applications of GaussianVTON are profound:
- Enhanced Online Shopping Experience: Shoppers can visualize themselves in outfits more realistically, helping them make better purchasing decisions.
- Fashion Design: Designers can use this tool to showcase their creations in a virtual space, making modifications and iterations easier and faster.
- Entertainment and Social Media: Creates opportunities in digital content creation, where creators can produce varied content involving digital fashion.
Speculating on Future Developments
Looking forward, the potential extensions of GaussianVTON could involve:
- Integration with AR and VR: Combining GaussianVTON with augmented and virtual reality technologies could lead to more immersive virtual try-on experiences.
- Personalization Algorithms: Enhancing the system with AI-driven personalization to suggest styles based on past preferences or body shapes.
- Eco-Friendly Fashion: By allowing users to preview clothes virtually, the technology could reduce returns and waste in the fashion industry.
Conclusion
GaussianVTON represents a significant step forward in the realm of virtual clothing try-on, particularly in how it handles the jump from 2D to 3D modeling while maintaining high fidelity in visual representations. It not only provides a robust framework for realistic garment fitting in virtual environments but also opens avenues for further research and development in both the technical and application domains of virtual try-on technologies.
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