AI Generates Realistic Sound for Video

HunyuanVideo-Foley tackles silent AI videos with synchronized audio generation.

A new AI model, HunyuanVideo-Foley, aims to solve the problem of silent AI-generated videos by creating high-fidelity, synchronized audio. This innovation uses a massive dataset and novel techniques to align sound perfectly with visual content and text prompts, enhancing immersion.

August 27, 2025

4 min read

AI Generates Realistic Sound for Video

Key Facts

  • HunyuanVideo-Foley is an end-to-end text-video-to-audio framework.
  • It synthesizes high-fidelity audio precisely aligned with visual dynamics and semantic context.
  • The model addresses multimodal data scarcity, modality imbalance, and limited audio quality.
  • It uses a scalable data pipeline with 100k-hour multimodal datasets.
  • HunyuanVideo-Foley achieves new state-of-the-art performance in audio fidelity and alignment.

Why You Care

Ever watched an AI-generated video, only to find it eerily silent? It breaks the magic, right? This common issue severely compromises immersion, according to the announcement. Now, imagine if those visuals came with perfectly synchronized, realistic sound. This new creation directly addresses that gap, making your AI video experiences far more engaging.

What Actually Happened

A team of researchers has introduced HunyuanVideo-Foley, a new artificial intelligence (AI) model. This model is designed to generate high-fidelity audio that precisely aligns with video dynamics and semantic context. The paper states that recent advancements in video generation produce visually realistic content. However, the absence of synchronized audio severely compromises immersion. HunyuanVideo-Foley aims to solve key challenges in video-to-audio generation. These challenges include multimodal data scarcity, modality imbalance, and limited audio quality in existing methods. The team revealed their approach incorporates three core innovations. These innovations help it synthesize accurate sound for videos.

Core Innovations of HunyuanVideo-Foley:

  1. ** Data Pipeline:** The system curates 100k-hour multimodal datasets. This is achieved through automated annotation, as detailed in the blog post.
  2. Representation Alignment Strategy: It uses self-supervised audio features. This guides latent diffusion training. The company reports this efficiently improves audio quality and generation stability.
  3. Novel Multimodal Diffusion Transformer: This resolves modal competition. It contains dual-stream audio-video fusion through joint attention. It also includes textual semantic injection via cross-attention, the technical report explains.

Why This Matters to You

This system has practical implications for anyone creating or consuming AI-generated content. Think about how much more impactful a short film or advertisement becomes with realistic sound effects. The study finds that HunyuanVideo-Foley achieves new performance. This covers audio fidelity, visual-semantic alignment, temporal alignment, and distribution matching. “The absence of synchronized audio severely compromises immersion,” as mentioned in the release. This means your viewers will feel more connected to the content. Do you want your AI-generated videos to truly captivate an audience?

Imagine you’re a content creator building a virtual world. With HunyuanVideo-Foley, the rustle of leaves will match the swaying trees. Footsteps will sound authentic on different surfaces. This makes the experience much more believable. For example, if you generate a video of a car driving, the engine sounds and tire screeches will automatically match the visual action. This eliminates the need for manual sound editing, saving you time and effort. Your projects will gain a professional polish, making them stand out.

The Surprising Finding

Here’s the twist: despite the complexity of generating synchronized audio, the research shows a surprising level of precision. The paper states that HunyuanVideo-Foley achieves new performance across audio fidelity, visual-semantic alignment, temporal alignment, and distribution matching. This is surprising because aligning sound perfectly with visual and textual cues is incredibly difficult. Many previous attempts struggled with modality imbalance and limited audio quality. This model uses a massive 100,000-hour multimodal dataset for training. This challenges the assumption that smaller, curated datasets are sufficient for such nuanced tasks. The sheer scale of data, combined with alignment strategies, allows for this accuracy. It means the system can handle subtle nuances in sound generation.

What Happens Next

We can expect to see this system integrated into various creative tools in the coming months. The company reports that demo pages are already available. This suggests a public release or API access could be available by late 2025 or early 2026. For example, video editing software might soon include an AI-powered sound generation feature. This would allow you to simply input your video and a text description, then receive a fully sound-designed clip. This will significantly impact industries like film production, gaming, and virtual reality. Content creators should start exploring how this high-fidelity audio generation can enhance their projects. It will allow you to create more immersive and believable digital experiences. The industry implications are vast, promising a new era of truly multimodal AI-generated content.