Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper

Recent video-to-audio (V2A) generation relies on extracting semantic and temporal features from video to condition generative models. Training these models from scratch is resource-intensive. Consequently, leveraging foundation models (FMs) has gained traction due to their cross-modal knowledge transfer and generalization capabilities. One prior work has explored fine-tuning a lightweight mapper network to connect a pre-trained visual encoder with a text-to-audio generation model for V2A. Inspired by this, we introduce the Multiple Foundation Model Mapper (MFM-Mapper). Compared to the previous mapper approach, MFM-Mapper benefits from richer semantic and temporal information by fusing features from dual visual encoders. Furthermore, by replacing a linear mapper with GPT-2, MFM-Mapper improves feature alignment, drawing parallels between cross-modal features mapping and autoregressive translation tasks. Our MFM-Mapper demonstrates high efficiency in terms of both data requirements and training costs. Compared to prior linear mapper-based work, it achieves superior performance in semantic and temporal consistency with only about 40% of the training data and 40% of the training epochs.