用于时尚姿势对齐和归一化的无训练、身份保留图像编辑
Training-Free, Identity-Preserving Image Editing for Fashion Pose Alignment and Normalization
AI总结：
针对时尚行业非刚性姿势调整难题，提出无训练的FashionRepose管道，结合预训练模型，通过零样本方法在保留服装身份特征的同时实现近实时编辑，已成功为OVS处理大量长袖服装。
摘要：
扩散模型为编辑现实世界物体图像带来了新可能，但非刚性变换物体（如修改姿势或应用基于图像的条件）仍面临挑战。在编辑中保留物体独特身份很复杂，当前技术在工业场景中精度不足。此外，调整扩散模型需要定制训练数据，现实中常无法获取。为填补这些空白，我们提出FashionRepose，一种专为时尚行业非刚性姿势调整设计的无训练新管道。它结合预训练现成模型修改长袖服装姿势，同时保留其身份和品牌特征。采用零样本方法，实现近实时编辑，完全无需专门训练数据。FashionRepose已为全球时尚公司OVS部署，处理超30000件长袖服装。
英文摘要：
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 FashionRepose, 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, FashionRepose enables near real-time edits, entirely eliminating the requirement for specialized training data. FashionRepose has been deployed for a global fashion firm, OVS, handling more than 30,000 long-sleeve garments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
