Apple proposes framework to align text, audio, and video in generative AI
Research introduces Cross-Referential Rewriter caption framework to reduce modal interference and bridge training-inference gaps in text-to-sounding-video generation.
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- Apple’s ML Research team proposes a method to generate videos with synchronized audio from text while aligning both modalities to the input conditions.
- Two challenges are identified: text conditioning bottlenecks and unclear optimal fusion mechanisms for cross-modal features.
- The paper introduces a dual-agent caption framework (Cross-Referential Rewriter) to disentangle training and inference captions and reduce modal interference.
Apple’s Machine Learning Research group describes a new approach to Text-to-Sounding-Video (T2SV) generation, where a model produces a video with synchronized audio from a text prompt, and both output streams are aligned to the input conditions. The authors argue that despite progress in joint audio-video training, two persistent challenges limit performance: text conditioning bottlenecks and unclear optimal fusion mechanisms for cross-modal feature interaction.
To address the first challenge, the team proposes the Cross-Referential Rewriter (CRR) caption framework, a dual-agent pipeline consisting of a Semantic Checker and a Cross-Modal Rewriter. The Semantic Checker extracts grounded Semantic Anchors, while the Cross-Modal Rewriter generates disentangled caption pairs for text-to-video (TV) and text-to-audio (TA) generation. This design aims to eliminate modal interference caused by shared captions and bridge the gap between dense training captions and concise user prompts used during inference.
The paper is slated for publication at ECCV in July 2026 and includes authors from Apple and Renmin University of China. It situates the work within broader research on rewritten captions for multimodal foundation models, citing prior studies on large-scale image–caption data and cross-modal representations in healthcare applications.
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