HomeTongyi Lab Unveils PrismAudio for Bidirectional Audio-Visual Separation
In an era where AI video generation is rapidly expanding, the disconnect between visuals and audio—often manifesting as silence or mismatched sound—remains a significant hurdle to viewer immersion. Addressing this challenge, Alibaba’s Tongyi Lab has unveiled PrismAudio, a novel video-to-audio framework. This research, accepted by the premier AI conference ICLR 2026, focuses on automatically synchronizing video content with precise ambient sound effects.
Reasoning Before Generation: Mastering Audio with "Chain of Thought"
Conventional audio generation models typically operate on an "intuitive" basis, frequently producing disjointed results—such as a horse’s hoofbeats sounding like bird chirps, or audio lagging behind the visual action. PrismAudio distinguishes itself by employing a "plan first, then generate" approach.
Decomposition Chain of Thought: Prior to generating audio, the model analyzes the video frame by frame: identifying scene elements, determining sound onset timing, assessing audio characteristics (e.g., crispness or depth), and locating the sound source spatially.
Multi-Dimensional Evaluation: To ensure high-quality output, the development team integrated reinforcement learning, utilizing four "virtual evaluators" to score results across semantic consistency, temporal synchronization, aesthetic quality, and spatial accuracy. This multi-faceted feedback loop resolves the common issue where previous models optimized for one aspect while neglecting others.
Lightweight and Efficient: Processing 9-Second Videos in 0.6 Seconds
Beyond accuracy, PrismAudio delivers exceptional speed. Leveraging its proprietary Fast-GRPO training algorithm, the model achieves a substantial performance boost while maintaining operational efficiency:
Compact Architecture, High Impact: With only 518 million parameters, the model is significantly smaller than comparable systems, which often require tens of billions of parameters.
Rapid Response: Generating high-quality audio for a 9-second video clip takes merely 0.63 seconds, approaching real-time performance.
Industry Perspective: The Rise of Authentic Ambient Audio
PrismAudio’s launch not only offers a robust automation tool for film post-production and short-form video creation but also inspires new approaches to multi-object generation. As AI improves its ability to balance audio texture and spatial positioning, future video production will increasingly achieve true "what you see is what you hear" fidelity.
Paper link: arXiv:2511.18833
Open source link: https://prismaudio-project.github.io/
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In an era where AI video generation is rapidly expanding, the disconnect between visuals and audio—often manifesting as silence or mismatched sound—remains a significant hurdle to viewer immersion. Addressing this challenge, Alibaba’s Tongyi Lab has unveiled PrismAudio, a novel video-to-audio framework. This research, accepted by the premier AI conference ICLR 2026, focuses on automatically synchronizing video content with precise ambient sound effects.
Reasoning Before Generation: Mastering Audio with "Chain of Thought"
Conventional audio generation models typically operate on an "intuitive" basis, frequently producing disjointed results—such as a horse’s hoofbeats sounding like bird chirps, or audio lagging behind the visual action. PrismAudio distinguishes itself by employing a "plan first, then generate" approach.
Decomposition Chain of Thought: Prior to generating audio, the model analyzes the video frame by frame: identifying scene elements, determining sound onset timing, assessing audio characteristics (e.g., crispness or depth), and locating the sound source spatially.
Multi-Dimensional Evaluation: To ensure high-quality output, the development team integrated reinforcement learning, utilizing four "virtual evaluators" to score results across semantic consistency, temporal synchronization, aesthetic quality, and spatial accuracy. This multi-faceted feedback loop resolves the common issue where previous models optimized for one aspect while neglecting others.
Lightweight and Efficient: Processing 9-Second Videos in 0.6 Seconds
Beyond accuracy, PrismAudio delivers exceptional speed. Leveraging its proprietary Fast-GRPO training algorithm, the model achieves a substantial performance boost while maintaining operational efficiency:
Compact Architecture, High Impact: With only 518 million parameters, the model is significantly smaller than comparable systems, which often require tens of billions of parameters.
Rapid Response: Generating high-quality audio for a 9-second video clip takes merely 0.63 seconds, approaching real-time performance.
Industry Perspective: The Rise of Authentic Ambient Audio
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