Sound-Guided Semantic Image Manipulation

By Seung Hyun Lee, Wonseok Roh, Wonmin Byeon, Sang Ho Yoon, Chanyoung Kim, Jinkyu Kim, Sangpil Kim. 2021

54 citations recorded by the source corpus.

The recent success of the generative model shows that leveraging the multi-modal embedding space can manipu-late an image using text information. However, manipulating an image with other sources rather than text, such as sound, is not easy due to the dynamic characteristics of the sources. Especially, sound can convey vivid emotions and dynamic expressions of the real world. Here, we propose a framework that directly encodes sound into the multi-modal (image-text) embedding space and manipulates an image from the space. Our audio encoder is trained to pro-duce a latent representation from an audio input, which is forced to be aligned with image and text representations in the multi-modal embedding space. We use a direct latent op-timization method based on aligned embeddings for sound-guided image manipulation. We also show that our method can mix different modalities, i.e., text and audio, which en-rich the variety of the image modification. The experiments on zero-shot audio classification and semantic-level image classification show that our proposed model outperforms other text and sound-guided state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition, Multimodal Learning, Signal Processing

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Citations in the corpus

GAN Inversion: A Survey (2021, background): “Lee et al. [156] propose a sound-guided image editing framework.”