Sound-Guided Semantic Image Manipulation

Semantically meaningful image manipulation often involves laborious manual human examination for each desired manipulation. Recent success suggests that leveraging the representation power of existing Contrastive Language-Image Pre-training (CLIP) models with the generative power of StyleGAN can successfully manipulate a given image driven by textual semantics. Following this, we explore adding a new modality, Sound, which can convey a different view of dynamic semantic information and thus can reinforce control strength over the semantic image manipulation. Our audio encoder is trained to produce a latent representation from an audio input, which is forced to be aligned with image and text representations in the same CLIP embedding space. Given such aligned embeddings, we use a direct latent optimization method so that an input image is modified in response to a user-provided sound input. We quantitatively and qualitatively demonstrate the effectiveness of our approach, and we observe our sound-guided image manipulation approach can produce semantically meaningful images.