FoleyGAN: Visually Guided Generative Adversarial Network-Based Synchronous Sound Generation in Silent Videos
This addresses the challenge of creating realistic soundtracks for silent videos, which is an incremental improvement in audio-visual generation.
The authors tackled the problem of generating sound synchronized with visual events in silent videos, achieving an average audio-visual synchronicity performance of 81% in human evaluations.
Deep learning based visual to sound generation systems essentially need to be developed particularly considering the synchronicity aspects of visual and audio features with time. In this research we introduce a novel task of guiding a class conditioned generative adversarial network with the temporal visual information of a video input for visual to sound generation task adapting the synchronicity traits between audio-visual modalities. Our proposed FoleyGAN model is capable of conditioning action sequences of visual events leading towards generating visually aligned realistic sound tracks. We expand our previously proposed Automatic Foley dataset to train with FoleyGAN and evaluate our synthesized sound through human survey that shows noteworthy (on average 81\%) audio-visual synchronicity performance. Our approach also outperforms in statistical experiments compared with other baseline models and audio-visual datasets.