Last updated on September 5th, 2025 at 07:48 pm
Imagine watching a short video of a roaring waterfall that looks incredibly realistic, yet you hear no sound. The silence is unsettling, because in real life our eyes and ears always work together. Foley art is the craft of adding everyday sounds—footsteps, wind, rustling leaves—to films and videos to recreate that sense of reality. Hunyuan Video‑Foley is a new artificial‑intelligence tool that brings this skill into the digital age. It automatically generates high‑fidelity soundtracks for videos by analysing the visuals and optional text prompts. The result is a realistic audio experience that matches the action on screen.
This guide offers a clear, non‑technical look at what Hunyuan Video‑Foley does, how it works, and why it matters. Whether you are a content creator, filmmaker, gamer, student, or simply curious about the latest AI developments, you’ll learn how this technology can make your silent clips come alive.
Understanding the basics of Foley
What is Foley art?
Foley art is the practice of recording and performing everyday sounds to enhance a video or film. If you’ve ever wondered who makes the sound of a sword being drawn, a door creaking open, or footsteps on gravel, the answer is a Foley artist. They recreate these noises in a studio using props and creative techniques, matching the timing of the actions in the footage.
The term comes from Jack Foley, a sound effects pioneer who developed the craft in the early days of radio and film. Today, Foley work is still essential in professional productions because on‑set audio often misses subtle sounds. For example, a field recording might capture actors’ dialogue but not the gentle swish of clothing when they move. Without Foley, a scene can feel flat or unrealistic.
Three types of Foley sounds
There are three main categories of Foley sounds:
- Feet – footsteps and impacts. Artists wear different shoes and walk on varied surfaces to reproduce the sound of boots on gravel, heels on wood, or sneakers on pavement.
- Movement – subtle sounds of clothes rustling or bodies shifting. These quiet noises make characters’ actions feel natural.
- Specifics – distinct noises like breaking glass, a key turning, or the clink of cutlery. Artists use creative substitutes—coconut halves for horse hooves, cellophane for crackling fire—when real objects are impractical.
By combining these elements, Foley artists build soundscapes that draw audiences into the story.
Why sound matters in visual storytelling
Sound is half of the sensory experience when we watch videos. It conveys mood, signals actions, and adds depth to visuals. Without it, even the most visually stunning scene feels distant. Foley artists painstakingly craft soundscapes to ensure every footstep, rustle, or crash is in sync with the motion on screen.
In user‑generated videos and AI‑generated clips, however, there is often no budget or time for manual sound design. As a result, many AI videos remain silent or come with generic background music that does not match the visuals. Viewers notice the disconnect immediately—it breaks immersion and makes the content feel artificial. This is where Hunyuan Video‑Foley comes in.
Introducing Hunyuan Video‑Foley
A breakthrough from Tencent Hunyuan lab
Hunyuan Video‑Foley is an AI model developed by Tencent’s Hunyuan research team. It is open source and designed specifically to generate synchronized sound effects for videos. The model’s full name is “HunyuanVideo‑Foley: Multimodal Diffusion with Representation Alignment for High‑Fidelity Foley Audio Generation.” While the name might sound complex, the core idea is simple: give the AI a silent video and an optional text description, and it will produce a realistic soundtrack that matches the visuals.
Who it is for
The tool is aimed at video creators, filmmakers, animators, advertisers, game developers, and anyone else who needs high‑quality sound for their videos. Because it’s open source, hobbyists and students can also experiment with it without spending money on proprietary software. The technology is especially useful for short videos, social media clips, indie films, and game cut‑scenes where hiring a professional Foley artist would be costly or impractical.
How it differs from traditional Foley
Traditional Foley relies on human artists to watch a film and perform sounds in real time. It is a creative and labor‑intensive process requiring expertise and a studio full of props. Hunyuan Video‑Foley, on the other hand, generates audio with machine learning. The AI has been trained on a massive dataset of videos, sounds, and text descriptions. It can identify what is happening in a scene—like a car driving past or waves crashing—and synthesize matching audio.
Although the model aims to replicate the realism of human‑made Foley, there are some differences. A human artist might decide to exaggerate certain sounds for dramatic effect or use unusual objects to create unique textures. The AI tries to match visuals precisely and produce sounds that blend naturally. This makes it ideal for cases where accurate synchronization and consistency are more important than artistic interpretation.
The technology behind Hunyuan Video‑Foley (in plain language)
A multimodal challenge
Generating sound for video is harder than simply converting one form of media into another. Videos contain both visual information and sometimes text prompts that describe the desired mood or context. Traditional AI models often focused more on the text prompt and ignored subtle cues in the video. This led to mismatched audio—for instance, a beach scene described with only “sound of waves” might ignore footsteps and birds.
Hunyuan Video‑Foley solves this by balancing information from multiple sources:
- Video frames – The AI analyses each frame to understand objects, actions, and timing. It looks at motion patterns—like when a ball hits the ground—so the corresponding sound happens at the exact moment.
- Text descriptions – If provided, a short prompt helps guide the mood or specify certain sounds (for example, “gentle rain” or “city traffic”). The model combines this with visual input rather than prioritizing one over the other.
- Audio knowledge – It uses pre‑trained audio encoders that understand the qualities of different sounds. This helps the system generate realistic timbres and textures.
A massive dataset
One of the biggest obstacles to good audio generation is data scarcity. High‑quality videos with well‑synchronized sound are rare, especially at the scale needed to train large models. The Hunyuan team built a library containing 100,000 hours of video, audio, and text descriptions. They used an automated pipeline to filter out low‑quality clips, such as those with long silences or heavily compressed audio, ensuring the AI learned from the best examples.
Representation alignment: teaching the AI to listen
To achieve high‑quality sound, Hunyuan Video‑Foley uses a training strategy called representation alignment. Imagine a student writing an essay while a teacher constantly compares it to a published book. If the student’s writing veers off, the teacher guides them back on track. Similarly, during training, the AI’s output is compared to features from a pre‑trained audio model that already knows what good sound looks like. This self‑supervised guidance helps the AI generate cleaner, richer, and more stable audio.
A hybrid diffusion transformer architecture
Under the hood, the model relies on a multimodal diffusion transformer. Without going deep into technical details, here’s what that means in simple terms:
- Diffusion process – The AI starts with a rough audio “sketch” containing noise and gradually refines it through many steps, similar to how an artist might draft and then detail a painting. At each step, the AI uses hints from the video and text to improve the sound.
- Multimodal transformer blocks – These components process visual and audio information simultaneously. They ensure that the rhythm and timing of the sound match the movement in the video.
- Unimodal transformer blocks – These focus purely on audio refinement. After syncing with the visual cues, the model polishes the sound quality and makes sure it has the right tone, loudness, and clarity.
- Temporal alignment – A special module called Synchformer handles frame‑level synchronization. It ensures that audio events like impacts or footsteps occur precisely when the corresponding visual events happen.
By combining these innovations, Hunyuan Video‑Foley overcomes earlier problems such as sound lag, wrong timing, or missing details.
Why Hunyuan Video‑Foley matters
Closing the gap in AI‑generated videos
Many AI tools can now create convincing videos from text prompts, but they often lack sound. Adding generic background music rarely fixes the problem because it does not match the action. Without proper audio, the illusion breaks, and the content feels unfinished. Hunyuan Video‑Foley solves this by automatically adding appropriate sounds that match both content and mood.
Time savings for creators
Foley is traditionally time‑consuming. A professional might spend hours matching each sound to the on‑screen action. With this AI tool, a creator can generate a complete soundtrack in minutes. The result is not perfect for every use case, but it provides a strong foundation. Artists can then tweak or layer additional sounds if needed.
Accessibility and democratization
Because the model is open source, anyone with basic technical skills can run it locally. You don’t need an expensive studio or a team of sound engineers. This democratizes access to high‑quality Foley, helping independent filmmakers, small businesses, educators, and students add professional polish to their projects.
Expanding creative possibilities
With automated Foley, creators can experiment more freely. They can produce multiple versions of a scene with different moods or sound styles simply by changing the text prompt. For example, a forest scene could have “calm ambience with birds and soft wind” or “ominous atmosphere with distant thunder.” The ability to quickly explore variations encourages experimentation and creativity.
Industry implications
Beyond film and video, this technology could impact advertising, gaming, virtual reality, and augmented reality. Games, for instance, require dynamic audio that changes based on user interaction. Automatic Foley could help generate base layers of sound effects, which designers can then adapt for interactive experiences. In virtual reality, perfectly aligned audio enhances immersion and can make environments feel more real.
How to use Hunyuan Video‑Foley: a step‑by‑step guide
1. Set up your environment
The official model is hosted on GitHub and Hugging Face. To use it locally, you’ll need a computer with a recent version of Python (3.8 or later) and an NVIDIA graphics card that supports CUDA 11.8 or 12.4. These requirements ensure the AI can run efficiently.
Open a terminal and clone the repository:
git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley
cd HunyuanVideo-Foley
Create a virtual environment and install dependencies:
python3 -m venv hunyuan_env
source hunyuan_env/bin/activate
pip install -r requirements.txt
Next, download the pretrained model weights. You can use Git Large File Storage (LFS) or the Hugging Face CLI:
# via git-lfs
git clone https://huggingface.co/tencent/HunyuanVideo-Foley
# via Hugging Face CLI (if installed)
huggingface-cli download tencent/HunyuanVideo-Foley
2. Prepare your video and prompt
Before generating sound, choose a video clip (without audio) and think about what kind of atmosphere you want. The tool accepts a simple text prompt to guide the output. For example:
- “Gentle ocean waves with footsteps on sand and seagulls calling.”
- “City street at night with light rain and distant traffic.”
- “Forest ambience with birdsong and leaves rustling.”
A well‑written prompt can influence the mood and help the AI decide which sounds to include.
3. Run the inference script
To generate Foley for a single video:
python3 infer.py \
--model_path PATH_TO_MODEL_WEIGHTS \
--config_path ./configs/hunyuanvideo-foley-xxl.yaml \
--single_video PATH_TO_YOUR_VIDEO.mp4 \
--single_prompt "Your text description" \
--output_dir ./output
Replace PATH_TO_MODEL_WEIGHTS with the directory where you downloaded the weights and PATH_TO_YOUR_VIDEO.mp4 with the actual video file. The script will generate a new audio file synchronized with your clip and save it in the output directory.
For batch processing multiple videos, prepare a CSV file listing each video path and its corresponding prompt. Then run:
python3 infer.py \
--model_path PATH_TO_MODEL_WEIGHTS \
--config_path ./configs/hunyuanvideo-foley-xxl.yaml \
--csv_path ./my_videos.csv \
--output_dir ./output_batch
4. Use the interactive web interface
If you prefer not to work in the terminal, the project includes a Gradio web app. After setting the environment variable to point to your model weights, run:
export HIFI_FOLEY_MODEL_PATH=PATH_TO_MODEL_WEIGHTS
python3 gradio_app.py
Visit the local URL provided in your terminal (usually http://localhost:7860). You can then upload videos, enter prompts, and generate sound effects through a browser interface.
5. Refine and edit
The generated audio might not be perfect for every scene. Listen closely and consider adding layers or adjusting volume levels in a digital audio workstation (DAW) like Audacity, Reaper, or Adobe Audition. You can also blend the AI‑generated sound with background music or dialogue.
Tips and best practices for beginners
- Start with simple prompts. Short descriptions like “calm forest ambience” or “busy marketplace” often yield better results than long, detailed sentences. You can add complexity once you see how the model responds.
- Use high‑quality video. Clear visuals with good lighting help the model identify objects and actions. Blurry or dark footage may lead to mismatched sounds.
- Match your prompt to the scene. The text should reflect what is actually happening in the video. If your scene shows both rain and thunder but you only mention rain, the AI may ignore thunder.
- Experiment with mood. Try variations of your prompt to see how the atmosphere changes. For example, “peaceful rain” versus “stormy rain” can produce very different audio textures.
- Layer sounds if necessary. The AI output is a strong foundation, but you can always add extra effects or music manually. This combination can produce a richer soundscape.
Pros and cons of using Hunyuan Video‑Foley
Advantages
- Time efficiency: Automates a process that typically requires extensive human effort.
- Consistency: Generates aligned audio across multiple clips, making it easier to maintain a cohesive sound signature.
- Accessibility: Open source and free to use, lowering the barrier for small creators and students.
- Versatility: Suitable for films, commercials, games, social media clips, educational videos, and more.
- Customizable mood: Optional text prompts let you steer the tone, intensity, and ambience of the sound.
Limitations
- Hardware requirements: Running the full model requires a powerful GPU and sufficient memory. Cloud services or local servers may be necessary for large workloads.
- Learning curve: Setting up the environment and understanding the inference script may be challenging for complete beginners without coding experience. However, the web interface makes it easier.
- Creative nuances: A human Foley artist can add artistic touches, exaggerate sounds for dramatic effect, or choose symbolic sounds. AI‑generated audio focuses on realism and might lack those creative flourishes.
- Language limitations: While the model supports multilingual text prompts, it may not perfectly understand highly descriptive or idiomatic language. Keeping prompts simple improves results.
Real‑world applications and case studies
Independent filmmaking
Indie filmmakers often have limited budgets for sound design. Hunyuan Video‑Foley can save them significant time and resources. For example, a short film with scenes set in a forest, a city street, and a cozy kitchen could receive appropriate soundscapes quickly. The director might then fine‑tune the audio by adding music or dialogue.
Advertising and marketing
Brands rely on attention‑grabbing visuals and sound to convey messages. An advertisement showing a product in different environments could benefit from this tool. Imagine a beverage commercial: the sound of fizzing, pouring, and clinking glasses can be synthesized to match each shot. This enhances the sensory impact without needing to record fresh audio.
Social media content creators
Platforms like TikTok, Instagram, and YouTube thrive on short videos. Many creators use AI tools to generate visuals but struggle with sound. Hunyuan Video‑Foley lets them produce consistent audio quickly, making their content more engaging. They can experiment with different prompts to suit various moods—playful, dramatic, or relaxed—and test what resonates with audiences.
Gaming and interactive media
Video games and interactive experiences rely heavily on sound. Game developers can use AI‑generated Foley to prototype soundscapes or create base layers of audio. For example, the sound of footsteps on different terrains or environmental noises (wind in a desert, dripping water in a cave) can be generated quickly. Designers can then build on these layers to add interactive elements such as weapon sounds or character voices.
Education and training
Students learning film or game design often focus on visuals and neglect sound due to time constraints. This tool provides an easy way to add audio, helping them understand the importance of sound in storytelling. Teachers can use it to demonstrate how different audio choices affect the mood of the same scene.
Comparing Hunyuan Video‑Foley to other tools
Several other AI models aim to generate sound for videos, each with its own strengths and weaknesses. Hunyuan Video‑Foley stands out for its balanced approach to video, audio, and text data. Other models might rely heavily on text prompts or produce low‑quality audio. Here’s a brief comparison:
- FoleyGrafter: Generates Foley sounds by grafting existing audio samples onto video, but may struggle with complex scenes. Its audio fidelity and timing are generally lower.
- V‑AURA and Frieren: Focus on visual and audio alignment but often lag behind in overall sound quality and semantic matching.
- MMAudio and ThinkSound: Provide decent sound quality but may not always align with the video’s timing and context.
Across common evaluation metrics—audio quality, timing accuracy, and semantic alignment—Hunyuan Video‑Foley demonstrates state‑of‑the‑art performance. It achieves higher scores in objective benchmarks and is rated more positively by human listeners. This doesn’t mean it’s perfect for every task, but it does show the impact of the innovations described earlier: a large training dataset, representation alignment, and a hybrid architecture.
The future of AI‑generated sound and Foley
Towards fully multimodal storytelling
As AI continues to improve, we’re moving closer to systems that can generate entire audiovisual experiences from simple instructions. Text‑to‑video models are already producing impressive visuals. With tools like Hunyuan Video‑Foley, those videos can now have matching sound effects. Eventually, we may see platforms that combine text‑to‑video and video‑to‑audio seamlessly—allowing creators to produce complete short films with a single prompt.
Improvements to quality and control
Future research will likely focus on giving users more control over the generated sound. For example, adjusting the volume of certain elements (wind versus footsteps) or changing the microphone “perspective” (close‑up versus distant). Developers might also integrate machine‑learning models that specialise in particular sound types, like music or speech, enabling a richer mixture of audio layers.
Real‑time applications
While Hunyuan Video‑Foley currently generates sound for pre‑recorded videos, there is potential for real‑time implementations. Imagine live‑streaming platforms where AI adds ambient sound to user‑generated videos on the fly. In gaming, the technology could adapt to the player’s actions, generating bespoke sound effects as the game unfolds.
Ethical considerations
As with all AI‑generated media, there are ethical questions to address. Content creators should be transparent about using AI in their work, especially in commercial projects. There is also a need to respect copyright when using training datasets—models should be built on licensed or public‑domain content. Finally, human Foley artists bring creativity and nuance that AI cannot fully replace. The goal should be to augment human creativity rather than eliminate it.
Conclusion
Hunyuan Video‑Foley represents a significant step forward in bringing realistic sound to AI‑generated videos. By combining visual analysis, text guidance, and audio knowledge, it solves the long‑standing problem of silent or mismatched audio in automatically created videos. Its large dataset, representation alignment technique, and hybrid diffusion transformer architecture allow it to produce synchronized, high‑fidelity soundscapes.
For creators of all kinds—from filmmakers and advertisers to hobbyists and students—this tool offers a way to add professional polish to their projects. It democratizes access to Foley art, reduces production time, and invites experimentation. While it doesn’t replace human creativity, it provides a powerful new medium for storytelling. As AI technology evolves, we can expect even more immersive and interactive audiovisual experiences. Whether you’re making a quick social media clip or a short film, Hunyuan Video‑Foley can help your visuals come alive with sound.
