Despite the rapid progress in image generation, emotional image editing
remains under-explored. The semantics, context, and structure of an image can
evoke emotional responses, making emotional image editing techniques valuable
for various real-world applications, including treatment of psychological
disorders, commercialization of products, and artistic design. For the first
time, we present a novel challenge of emotion-evoked image generation, aiming
to synthesize images that evoke target emotions while retaining the semantics
and structures of the original scenes. To address this challenge, we propose a
diffusion model capable of effectively understanding and editing source images
to convey desired emotions and sentiments. Moreover, due to the lack of emotion
editing datasets, we provide a unique dataset consisting of 340,000 pairs of
images and their emotion annotations. Furthermore, we conduct human
psychophysics experiments and introduce four new evaluation metrics to
systematically benchmark all the methods. Experimental results demonstrate that
our method surpasses all competitive baselines. Our diffusion model is capable
of identifying emotional cues from original images, editing images that elicit
desired emotions, and meanwhile, preserving the semantic structure of the
original images. All code, model, and data will be made public.

通过扩展已有的图像生成研究，我们提出了一种新的情感诱发图像生成模型，旨在在保留原始场景的语义和结构的同时，生成能够引发目标情感的图像。我们使用扩散模型来有效地理解和编辑源图像，以传达所需的情感和情绪，通过进行人类心理物理实验和引入四种新的评估指标，我们系统地评估了所有方法，并证明了我们的方法超越了所有竞争基准。