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 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.

翻译：根据视频输入生成语义和时间对齐的音频内容已成为研究焦点，尤其是在文本到视频生成取得显著突破之后。本研究旨在深入探讨视频到音频生成范式，重点关注三个关键方面：视觉编码器、辅助嵌入和数据增强技术。我们从一个基于简单却出奇有效的直觉构建的基础模型出发，通过消融实验探索了多种视觉编码器和辅助嵌入方案。采用一个强调生成质量和视频-音频同步对齐的综合评估流程，我们证明了所提模型具备最先进的视频到音频生成能力。此外，我们深入分析了不同数据增强方法对提升生成框架整体性能的影响，展示了从语义和时间维度推进同步音频生成挑战的可能性。我们希望这些见解能为开发更真实、更准确的视听生成模型奠定基础。