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This paper introduces LAV (Latent Audio-Visual), a system that integratesEnCodec's neural audio compression with StyleGAN2's generative capabilities toproduce visually dynamic outputs driven by pre-recorded audio. Unlike previousworks that rely on explicit feature mappings, LAV uses EnCodec embeddings aslatent representations, directly transformed into StyleGAN2's style latentspace via randomly initialized linear mapping. This approach preserves semanticrichness in the transformation, enabling nuanced and semantically coherentaudio-visual translations. The framework demonstrates the potential of usingpretrained audio compression models for artistic and computationalapplications.

LAV: Audio-Driven Dynamic Visual Generation with Neural Compression and StyleGAN2