Abstract
This paper presents InteractEdit, a novel framework for zero-shot Human-Object Interaction (HOI) editing, addressing the challenging task of transforming an existing interaction in an image into a new, desired interaction while preserving the identities of the subject and object. Unlike simpler image editing scenarios such as attribute manipulation, object replacement or style transfer, HOI editing involves complex spatial, contextual, and relational dependencies inherent in humans-objects interactions. Existing methods often overfit to the source image structure, limiting their ability to adapt to the substantial structural modifications demanded by new interactions. To address this, InteractEdit decomposes each scene into subject, object, and background components, then employs Low-Rank Adaptation (LoRA) and selective fine-tuning to preserve pretrained interaction priors while learning the visual identity of the source image. This regularization strategy effectively balances interaction edits with identity consistency. We further introduce IEBench, the most comprehensive benchmark for HOI editing, which evaluates both interaction editing and identity preservation. Our extensive experiments show that InteractEdit significantly outperforms existing methods, establishing a strong baseline for future HOI editing research and unlocking new possibilities for creative and practical applications. Code will be released upon publication.
Abstract (translated)
本文介绍了InteractEdit，这是一种新颖的零样本人类-对象交互（HOI）编辑框架，旨在解决将图像中现有的交互转换为新的、期望的交互的任务，同时保持主体和客体的身份不变。与属性操控、物体替换或风格迁移等更简单的图像编辑场景不同，HOI 编辑涉及人类与物体之间复杂的空间、上下文和关系依赖性。现有方法往往过度适应源图像结构，限制了它们对新交互所需的重大结构修改的适应能力。为解决这一问题，InteractEdit 将每个场景分解为主体、客体和背景组件，并采用低秩适应（LoRA）和选择性微调来在保留预训练的交互先验的同时学习源图像的视觉身份。这种正则化策略有效地平衡了交互编辑与身份一致性。 我们进一步引入了IEBench，这是迄今为止最全面的HOI编辑基准测试工具，它评估了交互编辑和身份保持两个方面。我们的大量实验表明，InteractEdit 显著优于现有方法，在未来 HOI 编辑研究中建立了坚实的基础，并为创意和实用应用打开了新的可能性。论文发布后将公开代码。
URL
https://arxiv.org/abs/2503.09130
