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This paper introduces SYNCAD, a novel framework for generating synchronized and contextually aligned videos from audio inputs. The framework addresses the challenges of semantic and temporal synchronization that often limit traditional audio-driven video generation methods. SYNCAD achieves this through two key components: the SonoToken Transformer and Audio-Conditioned Temporal Sequences. The SonoToken Transformer leverages advanced audio models, such as YAMNet and BEATs, to extract semantic sound categories and temporal features, converting them into pseudo-text inputs compatible with pre-trained text-to-video architectures. Audio-Conditioned Temporal Sequences ensure precise frame-by-frame synchronization by dynamically balancing local and global audio cues, with an attentive pooling mechanism prioritizing critical audio components. Experimental results demonstrate SYNCAD’s superiority over baseline methods in video quality, semantic relevance, and temporal alignment, as evidenced by significant improvements in metrics like Inception Score, CLIP Similarity, and Temporal Warping Error. The framework holds promise for applications in multimedia production, educational tools, and interactive entertainment, with future work focusing on integrating text-based guidance and enhancing synchronization precision.

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SYNCAD: Synchronised Yields from Narrative Cross Modal Audio and Data

  • 会议论文

  • 发表日期：

    2026年

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  • Aditya Vardhan Madivada,
  • Madhav Kartheek Bhumireddi,
  • K. E. Srinivasa Desikan

摘要

This paper introduces SYNCAD, a novel framework for generating synchronized and contextually aligned videos from audio inputs. The framework addresses the challenges of semantic and temporal synchronization that often limit traditional audio-driven video generation methods. SYNCAD achieves this through two key components: the SonoToken Transformer and Audio-Conditioned Temporal Sequences. The SonoToken Transformer leverages advanced audio models, such as YAMNet and BEATs, to extract semantic sound categories and temporal features, converting them into pseudo-text inputs compatible with pre-trained text-to-video architectures. Audio-Conditioned Temporal Sequences ensure precise frame-by-frame synchronization by dynamically balancing local and global audio cues, with an attentive pooling mechanism prioritizing critical audio components. Experimental results demonstrate SYNCAD’s superiority over baseline methods in video quality, semantic relevance, and temporal alignment, as evidenced by significant improvements in metrics like Inception Score, CLIP Similarity, and Temporal Warping Error. The framework holds promise for applications in multimedia production, educational tools, and interactive entertainment, with future work focusing on integrating text-based guidance and enhancing synchronization precision.