Video-to-Audio Generation with Hidden Alignment
Manjie Xu, Chenxing Li, Yong Ren, Rilin Chen, Yu Gu, Wei Liang, Dong Yu
2024-07-11
Summary
This paper talks about a new method for generating audio that matches silent videos, focusing on how to create sounds that are both meaningful and timed correctly with the visuals. The researchers introduce a model called VTA-LDM that learns to connect what we see in a video with the sounds that should accompany it.
What's the problem?
The main problem is that creating audio from video is challenging because it requires the audio to not only be relevant to the visuals but also to be synchronized in time. Previous methods struggled with this task, often producing audio that didn't match well with the video content or was out of sync.
What's the solution?
To solve this issue, the authors developed a model called VTA-LDM, which uses advanced techniques to analyze videos and generate corresponding audio. They focus on three key areas: using different ways to extract visual information (vision encoders), incorporating extra data to help the model understand context better (auxiliary embeddings), and applying methods to improve the training process (data augmentation). Their approach allows the model to produce high-quality audio that aligns well with the video, achieving state-of-the-art performance compared to earlier methods.
Why it matters?
This research is important because it enhances how we can create audio for videos, which is useful in many fields like film production, video games, and virtual reality. By improving the quality and synchronization of generated audio, this work could lead to more immersive and realistic experiences for viewers, making media content more engaging.
Abstract
Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthrough in text-to-video generation. In this work, we aim to offer insights into the video-to-audio generation paradigm, focusing on three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. Beginning with a foundational model VTA-LDM built on a simple yet surprisingly effective intuition, we explore various vision encoders and auxiliary embeddings through ablation studies. Employing a comprehensive evaluation pipeline that emphasizes generation quality and video-audio synchronization alignment, we demonstrate that our model exhibits state-of-the-art video-to-audio generation capabilities. Furthermore, we provide critical insights into the impact of different data augmentation methods on enhancing the generation framework's overall capacity. We showcase possibilities to advance the challenge of generating synchronized audio from semantic and temporal perspectives. We hope these insights will serve as a stepping stone toward developing more realistic and accurate audio-visual generation models.