Sound-Guided Semantic Video Generation

TL;DR

This paper introduces a novel sound-guided framework for semantic video generation that leverages multimodal embedding spaces and a sound inversion module to produce high-quality, semantically consistent videos.

Contribution

It proposes a new method combining sound inversion and CLIP-based embeddings to generate videos aligned with audio, advancing semantic video synthesis.

Findings

Outperforms state-of-the-art in video quality

Provides a new high-resolution landscape video dataset

Demonstrates effective applications in editing

Abstract

The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually not semantically meaningful due to the difficulty of determining the direction and magnitude in the StyleGAN latent space. In this paper, we propose a framework to generate realistic videos by leveraging multimodal (sound-image-text) embedding space. As sound provides the temporal contexts of the scene, our framework learns to generate a video that is semantically consistent with sound. First, our sound inversion module maps the audio directly into the StyleGAN latent space. We then incorporate the CLIP-based multimodal embedding space to further provide the audio-visual relationships. Finally, the proposed frame generator learns to find the trajectory in the latent space which is coherent with the corresponding sound and generates a video in a hierarchical manner. We provide the new high-resolution landscape video dataset (audio-visual pair) for the sound-guided video generation task. The experiments show that our model outperforms the state-of-the-art methods in terms of video quality. We further show several applications including image and video editing to verify the effectiveness of our method.