HunyuanPortrait: Bringing Static Portraits to Life with Advanced Animation Technology

In today’s digital age, portrait animation technology has emerged as a fascinating field with applications spanning across various industries. From Hollywood blockbusters to social media content creation, the ability to generate lifelike and temporally consistent portrait animations has become highly sought after. Among the myriad of technologies vying for attention, HunyuanPortrait stands out as a groundbreaking solution that promises to revolutionize how we create and interact with digital portraits.

Understanding HunyuanPortrait: The Basics

HunyuanPortrait represents a diffusion-based framework designed specifically for generating highly realistic and temporally coherent portrait animations. The technology operates on the principle of decoupling identity and motion—two fundamental elements that define portrait animation. By leveraging pre-trained encoders, HunyuanPortrait converts expressions and poses from driving videos into implicit control signals. These signals are then injected into a stabilized diffusion model backbone through attention-based adapters, enabling the creation of detailed and style-flexible animations from a single reference image.

What sets HunyuanPortrait apart from conventional approaches is its superior controllability and coherence. Unlike traditional methods that often struggle with maintaining consistent identity and smooth motion transitions, HunyuanPortrait delivers remarkable results that feel natural and visually appealing.

The Technical Framework Behind HunyuanPortrait

Pre-trained Encoders: The Foundation of Success

At the heart of HunyuanPortrait’s success lies its utilization of pre-trained encoders. These encoders play a pivotal role in extracting crucial expression and pose information from driving videos and transforming them into implicit control signals that the model can interpret.

For instance, the technology employs Arc2Face, a facial feature extraction model based on ArcFace. ArcFace has established itself as a powerhouse in facial recognition, capable of learning discriminative facial features. In the context of HunyuanPortrait, Arc2Face ensures that the generated animations maintain consistent identity with the reference image by accurately capturing facial characteristics.

Similarly, YoloFace is utilized for facial detection. Built upon the YOLO (You Only Look Once) series of object detection algorithms, YoloFace swiftly and accurately locates faces within video frames. This precise localization of facial regions is essential for subsequent expression and pose extraction processes.

Implicit Control Signals: Precision in Animation Control

The concept of implicit control signals is central to HunyuanPortrait’s operation. Rather than relying on explicit annotated data, the technology encodes expressions and poses from driving videos into a format that serves as an intermediate representation understandable by the model.

These signals capture nuanced facial feature changes, such as raised eyebrows, smiling mouths, and head movements. As conditional inputs, they combine with reference image features to guide the diffusion model in generating corresponding animation frames.

The advantage of this implicit control approach is threefold. First, it harnesses the powerful feature extraction capabilities of pre-trained models. Second, it eliminates the need for extensive explicitly annotated data. Third, by adjusting the weights and processing of these control signals, the technology allows for flexible manipulation of animation effects—such as amplifying or diminishing specific facial expressions.

Attention-Based Adapters: Seamlessly Integrating Multi-Source Information

Integrating information from different sources (reference image features and implicit control signals) presents a significant challenge in generating high-quality portrait animations. HunyuanPortrait’s attention-based adapters offer an elegant solution to this problem.

Attention mechanisms enable the model to learn correlations between different features and dynamically determine which information holds greater importance when generating a specific animation frame. For example, when creating a frame depicting a smiling expression, the attention mechanism may focus more on mouth region features from the reference image and corresponding expression change signals from the driving video, while reducing attention on less relevant areas.

These adapters are designed as lightweight modules inserted into the diffusion model backbone. They effectively integrate multi-source information without substantially increasing model complexity, allowing the diffusion model to produce diverse animation results based on varying input conditions.

The Stable Diffusion Model Backbone: Ensuring Generation Quality

The diffusion model backbone in HunyuanPortrait is meticulously designed and optimized to ensure the generation of high-quality animations. Typically comprising multiple residual blocks and attention layers, the model progressively refines image content—from rough outlines to intricate details—ultimately producing lifelike facial animations.

During training, the model learns from extensive real video data to capture various features and patterns of facial animations. To enhance its generalization capability, data augmentation techniques such as random cropping, rotation, and flipping are employed, enabling the model to generate high-quality animations under different input conditions.

Installation and Operation: Practical Guidelines for Implementation

Environment Preparation: Hardware and Software Requirements

Before diving into the installation and operation of HunyuanPortrait, it’s essential to ensure your computer meets the following hardware and software requirements:

  • Hardware: A computer equipped with an NVIDIA 3090 GPU supporting CUDA is required. The model has been tested on a single 24GB GPU, so adequate VRAM is crucial for running this model.

  • Software: The recommended operating system is Linux. Additionally, Python 3.x and related dependent libraries need to be installed.

Installation Process: A Detailed Step-by-Step Guide

Follow these steps to install HunyuanPortrait:

  1. Clone the project repository to your local machine using Git:

    • bash

      复制

git clone https://github.com/Tencent-Hunyuan/HunyuanPortrait

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  2. Install PyTorch and its related libraries:

     * ```bash
pip3 install torch torchvision torchaudio

  1. Install other project-dependent libraries:

    • bash

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pip3 install -r requirements.txt

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### Model Download: Acquiring Necessary Model Files

All model files are stored in the `pretrained_weights` directory by default. Here's how to download these model files:

  1. Install the Hugging Face CLI tool:

     * ```bash
pip3 install "huggingface_hub[cli]"

  1. Navigate to the pretrained_weights directory:

    • bash

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cd pretrained_weights

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  3. Download files related to Stable Video Diffusion:

     * ```bash
huggingface-cli download --resume-download stabilityai/stable-video-diffusion-img2vid-xt --local-dir . --include "*.json"

* The `--resume-download` parameter enables resumable downloads, ensuring stability. `--local-dir .` specifies downloading files to the current directory, and `--include "*.json"` filters to download only `.json` files.

4. Download the YOLO Face model file:

* ```bash

wget -c https://huggingface.co/LeonJoe13/Sonic/resolve/main/yoloface_v5m.pt

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     * `wget -c` supports resumable downloads, preventing interruptions due to network issues.

  5. Download the VAE (Variational Autoencoder) model file:

     * ```bash
wget -c https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors -P vae

* The `-P vae` parameter specifies saving the downloaded file to the `vae` directory.

6. Download the ArcFace model file:

* ```bash

wget -c https://huggingface.co/FoivosPar/Arc2Face/resolve/da2f1e9aa3954dad093213acfc9ae75a68da6ffd/arcface.onnx

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  7. Download specific HunyuanPortrait model files:

     * ```bash
huggingface-cli download --resume-download tencent/HunyuanPortrait --local-dir hyportrait

After downloading, your pretrained_weights directory structure should resemble the following:

bash

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.
├── arcface.onnx
├── hyportrait
│   ├── dino.pth
│   ├── expression.pth
│   ├── headpose.pth
│   ├── image_proj.pth
│   ├── motion_proj.pth
│   ├── pose_guider.pth
│   └── unet.pth
├── scheduler
│   └── scheduler_config.json
├── unet
│   └── config.json
├── vae
│   ├── config.json
│   └── diffusion_pytorch_model.fp16.safetensors
└── yoloface_v5m.pt

Running Examples: Witnessing the Magic of Technology

Once the installation and model download are complete, you can generate portrait animations using HunyuanPortrait. Here’s an example of how to run the code:

bash

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video_path="your_video.mp4"  # Replace your_video.mp4 with the path to your driving video file
image_path="your_image.png"  # Replace your_image.png with the path to your reference image file

python inference.py \
    --config config/hunyuan-portrait.yaml \
    --video_path $video_path \
    --image_path $image_path

Alternatively, you can execute the demo.sh script to run an example.

After running, you’ll find the generated portrait animation video in the specified output directory. This video showcases the animated results of the reference image character based on the expressions and poses from the driving video.

Advantages of HunyuanPortrait and Its Application Scenarios

Detailed Explanation of Advantages: Breakthroughs Compared to Traditional Methods

Unlike traditional GAN (Generative Adversarial Network) methods, which have achieved significant success in image generation but face limitations in portrait animation generation, HunyuanPortrait offers several advantages.

GAN-based methods typically require large amounts of paired data for training—reference images and corresponding animation videos—which are often difficult to obtain in practice. Moreover, GAN-generated animations may suffer from identity drift, where the character’s identity in the animation deviates from the reference image.

HunyuanPortrait’s diffusion model approach effectively addresses these issues. Diffusion models generate images through a gradual denoising process, enabling them to learn richer feature distributions with less training data. By incorporating pre-trained encoders and implicit control signals, HunyuanPortrait better preserves character identity and achieves more precise expression and pose control.

Additionally, HunyuanPortrait excels in temporal consistency. By introducing temporal processing modules within the diffusion model, it ensures highly coherent animations over time, avoiding issues like flickering and jittering common in traditional methods.

Application Scenarios: Diverse Fields from Entertainment to Professional Use

The applications of HunyuanPortrait are extensive, with some typical examples including:

  1. Virtual Avengers: In the realm of virtual streamers, HunyuanPortrait can generate animations for virtual characters based on the real-time expressions and movements of human performers. This enhances the richness and naturalness of virtual streamers’ expressions, making interactions with audiences more engaging.

  2. Film and Television Special Effects: In movie and TV production, HunyuanPortrait can create special effects animations for characters, such as magical transformations or facial expressions. This significantly improves the efficiency and quality of special effects production.

  3. Game Development: Game developers can utilize HunyuanPortrait to quickly generate character animations, particularly in role-playing games (RPGs), providing players with a more immersive experience.

  4. Social Media Content Creation: For content creators on social media platforms, HunyuanPortrait enables the easy creation of entertaining portrait animation videos, helping to attract more followers and attention.

Real-World Case Studies: Experiencing the Magic of Technology

To provide a more intuitive understanding of HunyuanPortrait’s capabilities, here are some highlights of practical case studies:

Portrait Singing Animation

The Portrait Singing case demonstrates how HunyuanPortrait generates singing animations based on a vocal video. The results show that the generated animation not only matches the lip movements to the audio but also features natural facial expressions that evolve with the singing, creating the illusion of a real singer performing.

Portrait Acting Animation

The Portrait Acting case presents animations of an actor transitioning between various expressions and movements. HunyuanPortrait successfully captures the actor’s subtle facial changes and accurately reflects them in the generated animations, allowing audiences to clearly perceive the character’s emotional shifts.

Portrait Making Faces Animation

In the Portrait Making Faces case, HunyuanPortrait creates a series of amusing facial expression animations. These animations vividly showcase a range of exaggerated facial expressions with smooth transitions, bringing joy to viewers.

For more impressive case studies, visit the HunyuanPortrait project page. I highly recommend exploring these examples to witness the power of this technology firsthand.

Related Technologies and Open-Source Projects: Learning and Contributing to the Community

HunyuanPortrait’s success is built upon the foundation of several excellent open-source projects. Here are some of the key technologies and projects that have significantly influenced HunyuanPortrait:

  1. Stable Video Diffusion (SVD): SVD, a video generation project based on diffusion models, provides the stable diffusion model framework for HunyuanPortrait. HunyuanPortrait builds on this foundation with targeted improvements and extensions to meet the specific needs of portrait animation generation.

  2. DiNOv2: DiNOv2 is a powerful self-supervised learning model whose exceptional image feature extraction capabilities have supported key information extraction in portrait animation generation. HunyuanPortrait has adopted some of DiNOv2’s technical approaches to enhance its understanding and utilization of portrait features.

  3. Arc2Face: As previously mentioned, Arc2Face, based on ArcFace, delivers efficient facial feature extraction, playing a crucial role in maintaining character identity consistency in generated animations.

  4. YOLO Face (YoloFace): YoloFace offers fast and accurate facial detection, laying the groundwork for key区域 localization in portrait animation generation.

In return, HunyuanPortrait actively contributes to the open-source community. Its code and pre-trained models are available on the Hugging Face platform for researchers and developers worldwide to use and reference. This spirit of open sharing helps drive the development and progress of the entire portrait animation technology field.

Future Outlook: Directions for Technological Evolution

Despite HunyuanPortrait’s remarkable achievements, portrait animation technology still holds vast potential for further development. Here are some possible future directions:

  1. Improved Real-Time Performance: Currently, HunyuanPortrait requires a certain amount of computation time to generate animations. For applications demanding high real-time performance, such as interactive virtual streamers, further optimization is needed. With advances in hardware technology and algorithmic improvements, faster animation generation is expected in the future.

  2. Expanded Expression and Motion Control: While HunyuanPortrait can already generate animations with a variety of expressions and motions, there is room for expansion. By incorporating more refined expression encoding and motion capture technologies, an even richer array of portrait animation effects can be achieved, catering to the demand for personalized animations across different fields.

  3. Integration with Other Technologies: Portrait animation technology can be deeply integrated with other cutting-edge technologies such as augmented reality (AR), virtual reality (VR), and natural language processing. For example, in AR scenarios, virtual characters generated through portrait animation technology can interact with the real environment in real time, offering users a more immersive experience. Combined with natural language processing technologies, virtual characters can respond to user voice commands with appropriate facial expressions and movements, enabling more intelligent human-computer interactions.

In summary, HunyuanPortrait opens the door to a new world of portrait animation technology brimming with potential. Its innovative technical framework and superior performance address numerous challenges faced by traditional methods and provide a robust foundation for future technological advancements. Whether for professional film and game production teams or everyday social media content creators, HunyuanPortrait has the potential to become an invaluable tool, helping bring our creativity and imagination to life through more vivid and realistic portrait animations.

If you’re interested in HunyuanPortrait and wish to delve deeper into this technology or give it a try, I encourage you to explore the detailed information available on its project page and Hugging Face page. I hope this article has provided you with a better understanding of HunyuanPortrait and that you’ll be inspired to witness its future applications.

  • References :

    • Xu, Zunnan et al. “HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation.” arXiv preprint arXiv:2503.18860 (2025).

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