SYNCAD: Cross Modal Diffusion Synchronized Yields from Narrative Cross Modal Audio and Data

By Aditya Vardhan & Madhav Kartheek | Published on January 15, 2024

Diffusion models designed to generate videos from text or audio inputs face significant challenges. The primary issue arises from the difficulty in accurately mapping descriptive information (e.g., text descriptions, audio cues) into video content that is both temporally coherent and semantically aligned. Current audio-video generation models, like MM-Diffusions, struggle with integrating diverse input modalities, limiting their interactivity and context-awareness.

To address these challenges, we introduce SYNCAD, a novel framework that leverages synchronized audio and data inputs to enhance video generation. SYNCAD employs a cross-modal diffusion model that integrates audio cues with structured data, enabling the generation of videos that are not only visually coherent but also contextually rich. This approach allows for more interactive and responsive video content creation, overcoming the limitations of existing models.The challenge lies in creating cross-modal diffusion models that not only map the input description (text/audio) to a temporally consistent video but also ensure that the generated video is semantically faithful to the input prompt (audio/text).