And now you’ve got a gorgeous AI video clip, but it’s eerily silent. That had long been the way things were. However, a new frontier in AI is addressing this challenge in an unprecedented way: Video-to-Audio (V2A) synthesis, the technology that allows AI to automatically synthesize synchronized, realistic audio sounds directly from visual input. Making realistic audio that perfectly matches what is happening, the surroundings, and the mood of a video is an incredibly painful task. It takes a lot of training for the AI to grasp not just what’s being depicted visually, but also the suggested sounds — footsteps on gravel, water splashing, the background noise of a city street. New Methods To combine sound recognition, new models and algorithms, such as that of Sony AI, MMAudio (accepted at CVPR 2025, but see the references for more scientific articles) illustrate this exciting path and recent breakthroughs. These systems make heavy use of advanced deep learning methods, the type of end-to-end training often done with diffusion models that are common in image gen. They parse frames of footage, typically following a provided text prompt that describes the audio it’s supposed to produce, and it iteratively builds sound waves that align both temporally and contextually with the images. The possibilities for applications are extensive. V2A could automatically add sound effects and ambient noise to silent footage, create immersive audio for virtual environments, generate realistic audio descriptions for accessibility, and offer powerful new tools for filmmakers and content creators. Though still a field in the making, bridging the divide between sight and sound in the context of AI is essential to building a fully immersive next generation of AI media experiences.