This study focuses on a novel task in text-to-image (T2I) generation, namely
action customization. The objective of this task is to learn the co-existing
action from limited data and generalize it to unseen humans or even animals.
Experimental results show that existing subject-driven customization methods
fail to learn the representative characteristics of actions and struggle in
decoupling actions from context features, including appearance. To overcome the
preference for low-level features and the entanglement of high-level features,
we propose an inversion-based method Action-Disentangled Identifier (ADI) to
learn action-specific identifiers from the exemplar images. ADI first expands
the semantic conditioning space by introducing layer-wise identifier tokens,
thereby increasing the representational richness while distributing the
inversion across different features. Then, to block the inversion of
action-agnostic features, ADI extracts the gradient invariance from the
constructed sample triples and masks the updates of irrelevant channels. To
comprehensively evaluate the task, we present an ActionBench that includes a
variety of actions, each accompanied by meticulously selected samples. Both
quantitative and qualitative results show that our ADI outperforms existing
baselines in action-customized T2I generation.

本研究在文本转图像生成（T2I）中关注一项新的任务，即动作定制，提出了基于反演的 Action-Disentangled Identifier（ADI）方法来学习动作特定的识别器，克服了现有方法中低层特征偏好和高层特征纠缠的问题，实现了优于现有基准的动作定制 T2I 生成。