TARO：基于起始感知条件的时间步自适应表示对齐用于同步视频到音频合成
TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis
AI总结：
TARO通过时间步自适应表示对齐和起始感知条件，在流式变换器上实现高保真视频到音频合成，显著降低FD和FAD并提升对齐准确率。
摘要：
本文提出了时间步自适应表示对齐与起始感知条件（TARO），一种用于高保真和时间连贯的视频到音频合成的新框架。该框架基于流式变换器构建，这些变换器提供稳定的训练和连续变换，以增强同步性和音频质量。TARO引入了两项关键创新：（1）时间步自适应表示对齐（TRA），通过根据噪声调度动态调整对齐强度来对齐潜在表示，确保平滑演化并提高保真度；（2）起始感知条件（OAC），整合起始线索，这些线索作为音频相关视觉时刻的尖锐事件驱动标记，以增强与动态视觉事件的同步。在VGGSound和Landscape数据集上的大量实验表明，TARO优于先前方法，实现了相对降低53%的弗雷歇距离（FD）、降低29%的弗雷歇音频距离（FAD）以及97.19%的对齐准确率，突显了其卓越的音频质量和同步精度。
英文摘要：
This paper introduces Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning (TARO), a novel framework for high-fidelity and temporally coherent video-to-audio synthesis. Built upon flow-based transformers, which offer stable training and continuous transformations for enhanced synchronization and audio quality, TARO introduces two key innovations: (1) Timestep-Adaptive Representation Alignment (TRA), which dynamically aligns latent representations by adjusting alignment strength based on the noise schedule, ensuring smooth evolution and improved fidelity, and (2) Onset-Aware Conditioning (OAC), which integrates onset cues that serve as sharp event-driven markers of audio-relevant visual moments to enhance synchronization with dynamic visual events. Extensive experiments on the VGGSound and Landscape datasets demonstrate that TARO outperforms prior methods, achieving relatively 53% lower Frechet Distance (FD), 29% lower Frechet Audio Distance (FAD), and a 97.19% Alignment Accuracy, highlighting its superior audio quality and synchronization precision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
