EmIT: Emotional Interaction control in Text-to-image diffusion models

Although current work of text-to-Image generation can preliminarily generate images from the descriptions of human-object interactions, it fails to consider the emotions involved in human-object interactions. While people often experience emotions when using objects or interacting with them. Therefore, in this paper, we propose Emotional Interaction Generation task, a novel image generation task, which generates emotionally expressive human-object interaction images from given prompts, human-object interaction (HOI) region, and emotions. First, we construct a new emotional interaction dataset, called EmotionHOI, which including 47,776 images with content prompt, emotions and human-object interaction bounding box. Second, we propose an emotion-aware text-to-image diffusion model, named EmIT, for emotional interaction generation. Specifically, EmIT consists of three components: (1) an emotion interaction tokenizer that encodes subject, object, action, and emotion into structured tokens; (2) an Emo-Interaction Self-Attention that preliminarily guides the latent space to conduct hybrid learning with emotional interaction tokens; and (3) a Hierarchical Emotion-Visual Cross-Attention that further focus on grounding affect-such as pose, gaze, or interaction intensity-into specific spatial regions and capture subtle emotional variations. These components jointly model interaction semantics and emotional context, enabling EmIT to generate images that are both behaviorally coherent and emotionally expressive. Experimental results on the EmotionHOI dataset demonstrate the superiority of the proposed model.