无需训练，一键调整时装长袖衣摆姿势并保留品牌特征
Training-Free, Identity-Preserving Image Editing for Fashion Pose Alignment and Normalization
- 用现成模型组合实现零样本姿态调整
- 处理超3万件长袖服装，保持品牌标识不变
- 适合时尚行业快速批量修图，无需定制数据
扩散模型为真实物体图像编辑开辟了新可能，但对非刚性变形（如姿态修改或基于图像的条件生成）仍存在挑战。在编辑过程中保持物体独特身份尤为困难，现有技术难以满足工业场景对一致性的严苛要求。此外，适应扩散模型通常需要定制训练数据，而现实中往往不可得。为此，我们提出 FashionRepose——一种全新的无训练流程，专为时尚行业设计，用于处理长袖服饰的非刚性姿态调整。该方法结合预训练的现成模型，在不改变服饰身份与品牌特征的前提下，实现姿态修正。采用零样本策略，可实现近实时编辑，完全无需专用训练数据。FashionRepose已部署于全球时尚企业 OVS，成功处理超过30,000件长袖服装。
原文摘要 · Abstract (English)
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.
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