TA-V2A: Textually Assisted Video-to-Audio Generation
This addresses the problem of generating coherent audio from videos for applications in multimedia editing and automated content creation, representing an incremental improvement over existing methods.
The paper tackles the challenge of extracting precise semantic information from videos for video-to-audio generation by proposing TA-V2A, which integrates language, audio, and video features with large language models and diffusion models, resulting in improved semantic expression and temporal alignment.
As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation. While Transformer and Diffusion models have advanced audio generation, a significant challenge persists in extracting precise semantic information from videos, as current models often lose sequential context by relying solely on frame-based features. To address this, we present TA-V2A, a method that integrates language, audio, and video features to improve semantic representation in latent space. By incorporating large language models for enhanced video comprehension, our approach leverages text guidance to enrich semantic expression. Our diffusion model-based system utilizes automated text modulation to enhance inference quality and efficiency, providing personalized control through text-guided interfaces. This integration enhances semantic expression while ensuring temporal alignment, leading to more accurate and coherent video-to-audio generation.