SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound
This addresses the gap in integrating spatial audio cues for immersive content creation, which is incremental as it builds on existing neural generative models.
The paper tackles the problem of generating high-quality spatial audio to complement visual content for immersive experiences, achieving compelling results for videos, images, and dynamic media from the internet or learned approaches.
Generating combined visual and auditory sensory experiences is critical for the consumption of immersive content. Recent advances in neural generative models have enabled the creation of high-resolution content across multiple modalities such as images, text, speech, and videos. Despite these successes, there remains a significant gap in the generation of high-quality spatial audio that complements generated visual content. Furthermore, current audio generation models excel in either generating natural audio or speech or music but fall short in integrating spatial audio cues necessary for immersive experiences. In this work, we introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches.