Core Concepts
StableGarment introduces a unified framework for garment-centric generation tasks, leveraging Stable Diffusion models to achieve state-of-the-art results in virtual try-on applications.
Abstract
The content introduces StableGarment, a framework for garment-centric generation tasks using Stable Diffusion models. It addresses challenges in virtual try-on tasks by preserving intricate garment details and enabling flexible image creation. The framework includes a garment encoder, try-on ControlNet, and data engine to enhance model performance. Extensive experiments demonstrate superior results compared to existing methods.
Introduction
Advances in image generation with text-to-image diffusion models.
Impact on the fashion industry with photorealistic virtual try-on tasks.
Challenges Beyond Virtual Try-On
Limitations of existing methods in creating varied product visuals cost-effectively.
Dual demand for quick adjustments and accurate depiction of textures.
Proposed Solution: StableGarment
Unified framework addressing GC generation tasks with Stable Diffusion.
Garment encoder captures detailed features; ControlNet enables precise virtual try-ons.
Experiments and Results
Baseline comparisons for subject-driven and virtual try-on methods.
Evaluation metrics include SSIM, LPIPS, FID, KID, DINO-M, human preference scores.
Conclusion
StableGarment delivers SOTA results in virtual try-on tasks with broad applications.
Stats
Fig. 1: The proposed StableGarment can perform various garment-centric generation tasks.
Abstract mentions tackling GC generation tasks using Stable Diffusion models.