ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language
  Models for Audio Generation and Editing
Authors: name: Huadai Liu, name: Jialei Wang, name: Kaicheng Luo, name: Wen Wang, name: Qian Chen, name: Zhou Zhao, name: Wei Xue

While end-to-end video-to-audio generation has greatly improved, producing
high-fidelity audio that authentically captures the nuances of visual content
remains challenging. Like professionals in the creative industries, such
generation requires sophisticated reasoning about items such as visual
dynamics, acoustic environments, and temporal relationships. We present
ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning
to enable stepwise, interactive audio generation and editing for videos. Our
approach decomposes the process into three complementary stages: foundational
foley generation that creates semantically coherent soundscapes, interactive
object-centric refinement through precise user interactions, and targeted
editing guided by natural language instructions. At each stage, a multimodal
large language model generates contextually aligned CoT reasoning that guides a
unified audio foundation model. Furthermore, we introduce AudioCoT, a
comprehensive dataset with structured reasoning annotations that establishes
connections between visual content, textual descriptions, and sound synthesis.
Experiments demonstrate that ThinkSound achieves state-of-the-art performance
in video-to-audio generation across both audio metrics and CoT metrics and
excels in out-of-distribution Movie Gen Audio benchmark. The demo page is
available at https://ThinkSound-Project.github.io.