Training-free, Identity-preserving Image Editing for Fashion Pose Alignment and Normalization

Diffusion models have recently unlocked new possibilities in editing images of real-world objects. Yet, transforming objects in non-rigid ways, such as modifying poses or applying image-based conditioning, continues to present significant challenges. Retaining the unique identity of objects during these edits is a complex task, and current techniques often fall short of delivering the precision needed for industrial settings, where consistency is non-negotiable. Additionally, adapting diffusion models demands custom training data, which is often unavailable in real-world scenarios. To address these gaps, we present FASxiONREPOSE, a novel, training-free pipeline designed to handle non-rigid pose adjustments specifically for the fashion industry. This approach combines pretrained off-the-shelf models to modify the poses of long-sleeve garments while safeguarding their identity and branding characteristics. By adopting a zero-shot methodology, FASxiONREPOSE enables near real-time edits, entirely eliminating the requirement for specialized training data. FASxiONREPOSE has been deployed for a global fashion firm, OVS, handling more than 30,000 long-sleeve garments.