TLDR: Tencent’s Hunyuan lab has unveiled ‘Hunyuan Video-Foley,’ an innovative AI model designed to generate high-fidelity, synchronized audio for AI-created videos. This end-to-end text-video-to-audio (TV2A) framework addresses long-standing challenges like silent AI videos, modality imbalance, and limited audio quality. By leveraging a massive 100,000-hour multimodal dataset and a novel multimodal diffusion transformer, the system produces professional-grade sound effects. The open-source release is set to revolutionize creative industries such as filmmaking, game development, and advertising.
Tencent’s Hunyuan lab has announced a significant leap forward in artificial intelligence with the introduction of ‘Hunyuan Video-Foley,’ an end-to-end text-video-to-audio (TV2A) framework. This new AI model is engineered to produce lifelike and perfectly synchronized audio for AI-generated videos, a capability that has long been a critical missing piece in the immersive experience of automated content creation.
For years, AI-generated videos, despite their visual sophistication, have often suffered from an ‘eerie silence,’ lacking the intricate soundscapes known as Foley art. This absence of realistic sound effects—from the rustle of leaves to the clink of a glass—has severely compromised the immersion factor. The core challenges identified by researchers included multimodal data scarcity, modality imbalance (where AI prioritized text prompts over visual cues), and the generally subpar quality of existing AI-generated audio.
Tencent’s Hunyuan team tackled these issues through three core innovations:
1. Massive Multimodal Dataset: The team curated an extensive 100,000-hour library of high-quality video, audio, and text descriptions. This scalable data pipeline utilized automated annotation and rigorous filtering to eliminate low-quality content, ensuring the AI learned from optimal material.
2. Smarter Architecture with Dual-Stream Fusion: Hunyuan Video-Foley employs a novel multimodal diffusion transformer. This architecture enables the AI to first focus intensely on the visual-audio correlation to achieve precise temporal alignment, such as matching footsteps to the exact moment a shoe hits the ground. Subsequently, it integrates textual prompts to grasp the overall context and mood of the scene, preventing specific visual details from being overlooked.
3. Representation Alignment (REPA) for High-Fidelity Audio: To guarantee superior audio quality and generation stability, a training strategy called Representation Alignment (REPA) was implemented. This method guides the latent diffusion training by comparing the AI’s output to features from a pre-trained, professional-grade audio model, leading to cleaner, richer, and more stable sound.
Comprehensive evaluations have demonstrated that Hunyuan Video-Foley achieves new state-of-the-art performance across multiple metrics, including audio fidelity, visual-semantic alignment, temporal alignment, and distribution matching. Human listeners consistently rated its output as higher quality and better matched to the video action and timing compared to other leading AI models.
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The project, detailed in the arXiv paper ‘[2508.16930] HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation,’ lists Sizhe Shan, Qiulin Li, Yutao Cui, Miles Yang, Yuehai Wang, Qun Yang, Jin Zhou, and Zhao Zhong as authors. The open-source release of this professional-grade AI tool is expected to empower creators in various fields, including short video creation, film production, advertising, and game development, by bringing the magic of Foley art to automated content generation.
