Apple researchers unveil new method for synchronized text-to-video-audio generation

Researchers have developed a new framework called the Cross-Referential Rewriter (CRR) to improve text-to-sounding-video (T2SV) generation. This dual-agent system addresses challenges in aligning video and audio generation with text prompts by disentangling caption pairs for video and audio, thus reducing modal interference. The CRR aims to bridge the gap between detailed training captions and concise user prompts, leading to more synchronized and contextually relevant video and audio outputs. AI

IMPACT This research could lead to more realistic and synchronized audio-visual content generation from text prompts.

RANK_REASON Academic paper detailing a new method for T2SV generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

• Apple Inc.
• Cross-Referential Rewriter
• Kaisi Guan
• Meng Cao
• Peng Zhang
• Renmin University of China
• Ruihua Song
• Text-to-Sounding-Video
• Xiaojiang Liu
• Xihua Wang
• Xin Cheng
• Zhengfeng Lai

• paper
• product

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