Interact-Custom：定制化人物-物体交互图像生成
Interact-Custom: Customized Human Object Interaction Image Generation
AI总结：
本文提出定制化人物-物体交互图像生成任务及两阶段模型 Interact-Custom，通过先建模交互空间掩码再生成图像，实现身份保持与交互语义控制。
摘要：
组合式定制图像生成旨在在生成内容中定制多个目标概念，因其广泛应用而受到关注。现有方法主要集中于目标实体外观的保持，而忽略了目标实体之间细粒度的交互控制。为了使模型具备这种交互控制能力，我们聚焦于人物-物体交互场景，提出了定制化人物-物体交互图像生成（CHOI）任务，该任务同时要求保持目标人物与物体的身份特征，并对二者之间的交互语义进行控制。CHOI 面临两个主要挑战：（1）同时进行身份保持和交互控制要求模型将人物与物体分解为自包含的身份特征和面向姿态的交互特征，而当前的人物-物体交互图像数据集无法为这种特征分解学习提供理想样本；（2）人物与物体之间不恰当的空间配置可能导致所需交互语义的缺失。为了解决这些问题，我们首先处理了一个大规模数据集，其中每个样本包含同一对人物与物体在不同交互姿态下的图像。然后我们设计了一个两阶段模型 Interact-Custom，该模型首先通过生成描绘交互行为的前景掩码来显式建模空间配置，然后在该掩码的引导下生成目标人物与物体的交互图像，同时保持其身份特征。此外，如果用户提供了背景图像以及目标人物与物体应出现的联合位置，Interact-Custom 还提供指定这些内容的可选功能，从而提供较高的内容可控性。在我们为 CHOI 任务定制的评估指标上进行的大量实验证明了我们方法的有效性。
英文摘要：
Compositional Customized Image Generation aims to customize multiple target concepts within generation content, which has gained attention for its wild application. Existing approaches mainly concentrate on the target entity's appearance preservation, while neglecting the fine-grained interaction control among target entities. To enable the model of such interaction control capability, we focus on human object interaction scenario and propose the task of Customized Human Object Interaction Image Generation(CHOI), which simultaneously requires identity preservation for target human object and the interaction semantic control between them. Two primary challenges exist for CHOI:(1)simultaneous identity preservation and interaction control demands require the model to decompose the human object into self-contained identity features and pose-oriented interaction features, while the current HOI image datasets fail to provide ideal samples for such feature-decomposed learning.(2)inappropriate spatial configuration between human and object may lead to the lack of desired interaction semantics. To tackle it, we first process a large-scale dataset, where each sample encompasses the same pair of human object involving different interactive poses. Then we design a two-stage model Interact-Custom, which firstly explicitly models the spatial configuration by generating a foreground mask depicting the interaction behavior, then under the guidance of this mask, we generate the target human object interacting while preserving their identities features. Furthermore, if the background image and the union location of where the target human object should appear are provided by users, Interact-Custom also provides the optional functionality to specify them, offering high content controllability. Extensive experiments on our tailored metrics for CHOI task demonstrate the effectiveness of our approach.
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。
