Generating Realistic Images from In-the-wild Sounds

Taegyeong Lee, Jeonghun Kang, Hyeonyu Kim, Taehwan Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 7160-7170

Representing wild sounds as images is an important but challenging task due to the lack of paired datasets between sound and image data and the significant differences in the characteristics of these two modalities. Previous studies have focused on generating images from sound in limited categories or music. In this paper, we propose a novel approach to generate images from wild sounds. First, we convert sound into text using audio captioning. Second, we propose audio attention and sentence attention to represent the rich characteristics of sound and visualize the sound. Lastly, we propose a direct sound optimization with CLIPscore and AudioCLIP and generate images with a diffusion-based model. In experiments, it shows that our model is able to generate high quality images from wild sounds and outperforms baselines in both quantitative and qualitative evaluations on wild audio datasets.