VIDEO-TO-AUDIO GENERATION WITH HIDDEN ALIGNMENT

VIDEO-TO-AUDIO GENERATION WITH HIDDEN ALIGNMENT

10 Jul 2024 | Manjie Xu, Chenxing Li, Yong Ren, Rilin Chen, Yu Gu, Wei Liang, Dong Yu
The paper "Video-to-Audio Generation with Hidden Alignment" by Manjie Xu et al. focuses on the challenge of generating semantically and temporally aligned audio content from silent videos. The authors propose a foundational model called VTA-LDM, which leverages a Latent Diffusion Model (LDM) and a vision encoder to extract and interpret visual features from the input video. The key contributions of the paper include: 1. **Model Architecture**: The VTA-LDM framework is designed to generate audio content that aligns with the visual events in a video. 2. **Key Aspects**: The study explores three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. 3. **Ablation Studies**: Comprehensive ablation studies are conducted to evaluate the impact of different components on the model's performance. 4. **Evaluation**: The model is evaluated using various metrics, including semantic alignment, temporal alignment, and subjective quality assessments. 5. **Results**: The VTA-LDM model achieves state-of-the-art performance in video-to-audio generation tasks, demonstrating its effectiveness in generating high-quality, semantically and temporally aligned audio content. The paper also discusses the limitations and future directions, emphasizing the need for more extensive and diverse datasets to further improve the model's performance and generalizability. The authors highlight the potential social impact of their work, particularly in enhancing the accessibility of high-quality video-audio content and the ethical considerations surrounding the generation of realistic audio from silent visual input.The paper "Video-to-Audio Generation with Hidden Alignment" by Manjie Xu et al. focuses on the challenge of generating semantically and temporally aligned audio content from silent videos. The authors propose a foundational model called VTA-LDM, which leverages a Latent Diffusion Model (LDM) and a vision encoder to extract and interpret visual features from the input video. The key contributions of the paper include: 1. **Model Architecture**: The VTA-LDM framework is designed to generate audio content that aligns with the visual events in a video. 2. **Key Aspects**: The study explores three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. 3. **Ablation Studies**: Comprehensive ablation studies are conducted to evaluate the impact of different components on the model's performance. 4. **Evaluation**: The model is evaluated using various metrics, including semantic alignment, temporal alignment, and subjective quality assessments. 5. **Results**: The VTA-LDM model achieves state-of-the-art performance in video-to-audio generation tasks, demonstrating its effectiveness in generating high-quality, semantically and temporally aligned audio content. The paper also discusses the limitations and future directions, emphasizing the need for more extensive and diverse datasets to further improve the model's performance and generalizability. The authors highlight the potential social impact of their work, particularly in enhancing the accessibility of high-quality video-audio content and the ethical considerations surrounding the generation of realistic audio from silent visual input.
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