超越情感提示：由效价-唤醒-支配驱动的细粒度文本到图像生成
Beyond Emotion Prompts: Fine-Grained Text-to-Image Generation Driven by Valence-Arousal-Dominance
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中文总结 AI 辅助
本文提出EMOTRANS框架，利用VAD坐标实现细粒度情感控制的文本到图像生成，构建EMOVAD数据集并采用双分支训练，在保持内容对齐和图像质量的同时实现可感知、可排序的连续情感变化。
中文摘要 AI 辅助
尽管文本到图像模型能够准确描绘主体和场景，创作者仍然难以在不重写内容描述的情况下指定图像应传达的细粒度情感。自然语言可以暗示情感，但它无法提供具有稳定含义和有序强度的控制尺度。我们提出了EMOTRANS，它将基于心理学的效价-唤醒-支配（VAD）坐标转换为独立于内容文本的生成条件，并在去噪阶段进行调制，使情感风格成为可精细调节的创作变量。为支持这一目标，我们构建了EMOVAD，一个艺术绘画数据集，将客观内容描述与多位标注者分别收集的情感评分配对。我们还通过共享模型的双分支训练来协调情感表达和内容保持。客观和人工评估表明，该框架提高了三维情感控制的准确性，并产生可感知、可排序的连续变化，同时保持具有竞争力的文本对齐和图像质量。这项工作为图像生成提供了一种实用的情感驱动方法，将客观内容描绘扩展到细粒度的情感调整。
英文摘要
Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should convey without rewriting its content description. Natural language can suggest emotions, but it offers no control scale with stable meanings and ordered intensities. We propose EMOTRANS, which transforms psychologically grounded valence-arousal-dominance (VAD) coordinates into generation conditions that are independent of the content text and modulated across denoising stages, making emotional style a finely adjustable creative variable. To support this goal, we construct EMOVAD, an art-painting dataset that pairs objective content descriptions with separately collected emotional ratings from multiple annotators. We also coordinate emotional expression and content preservation through dual-branch training with a shared model. Objective and human evaluations show that the framework improves the accuracy of three-dimensional emotion control and produces perceptible, orderable continuous changes while maintaining competitive text alignment and image quality. This work provides a practical emotion-driven approach to image generation that extends objective content depiction to fine-grained emotional adjustment.
发表机构
- College of Information Science and Engineering（信息科学与工程学院）
- Hunan Normal University（湖南师范大学）
机构由 AI 辅助整理，请以论文原文为准。
