  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 VTA-LDM 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.
