LivePortrait is a framework for generating realistic portrait animations, which can generate dynamic videos from just a static portrait image. Its main goal is to achieve efficient and precisely controlled portrait animation, so that the generated animation reaches a high level in both visual effects and detail control.
It can generate vivid animated videos from a single image and can precisely control the movements of eyes and lips to ensure natural and smooth animation.
It can also handle the seamless stitching of multiple portraits, ensuring smooth transitions between multiple dynamic characters without any abrupt boundary effects.
What Problem LivePortrait Solve
Build quality and efficiency
Although the traditional diffusion model method has high generation quality, it has huge computational overhead and is difficult to achieve real-time processing. LivePortrait uses the implicit key point method to significantly improve computational efficiency while ensuring high quality.
Lack of controllability
Many existing methods lack fine control over details, such as independent motion control of eyes and lips. LivePortrait solves this problem through a specially designed retargeting module, making the animation more realistic in micro-expressions and detailed movements.
Actual Effect
In the animations generated by LivePortrait, facial expressions and head movements are natural and realistic, highly similar to actual character movements.
LivePortrait performs well in controlling the details of eyes and lips, and can accurately control the gaze direction of the eyes and the opening and closing movements of the lips.
Comparative experiments show that the animation quality generated by LivePortrait is better than the existing non-diffusion model and diffusion model methods.
On an RTX 4090 GPU, LivePortrait achieves a generation speed of 12.8 milliseconds per frame, significantly faster than existing diffusion model methods.
By optimizing the network architecture and using an efficient implicit keypoint method, LivePortrait significantly reduces computational overhead while ensuring generation quality.
processing of LivePortrait
Main Features of LivePortrait
Generate vivid animations from a single image
Function description: LivePortrait can generate vivid and realistic animations from a single static portrait image. By leveraging the appearance characteristics of the source image and the motion information of the driving video, this function can generate dynamic videos containing rich facial expressions and head posture changes.
We use a high-quality dataset for training, including 69 million high-quality images and video frames, to ensure that the model can generalize to a variety of scenarios.
Implicit keypoints are introduced as intermediate motion representation to balance generation quality and computational efficiency.
For example : If you have a static photo of a person, LivePortrait can generate an animation of the person smiling, blinking, or turning his head.
Precise control of eye movements
Function description: LivePortrait has a built-in eye redirection module that can independently control the movement of the eyes. This function allows the eyes to move freely as needed in the generated animation, showing different gaze directions and blinking movements.
For example : When generating an animation, you can make the character’s eyes scan from left to right, or show the character’s blinking as needed to enhance the realism of the animation.
Precise control of lip movements
Function description: LivePortrait’s lip redirection module can accurately control the opening and closing of the lips, making the character’s lip movements in the animation synchronized with speech or expression changes, making the performance more natural.
For example: when generating an animation of a person speaking, the lips can be precisely synchronized with the input voice or text content to simulate natural speaking movements.
Stitching module
Function description: The stitching module is used to process the seamless stitching between multiple portraits. This function ensures smooth transitions between multiple dynamic characters without abrupt boundary effects.
For example: When you need to generate an animation containing multiple characters, the stitching module can make the transition between the characters natural and smooth, avoiding inconsistent boundaries.
Support for multiple portrait styles
Functional description: LivePortrait supports the generation of portrait animations in various styles by mixing image and video training strategies. Whether it is a realistic style or anime style portrait, it can generate high-quality animations.
For example: Whether it is a real person in a photo or an anime-style portrait, LivePortrait can generate a dynamic video of the corresponding style, making the animation suitable for a variety of application scenarios.
High-resolution animation generation
Functional Description: Using the SPADE decoder and PixelShuffle upsampling layer, LivePortrait can generate high-resolution animations and improve image clarity and detail.
For example: The generated animation can reach a resolution of 512×512, making the facial details of the characters clearer and suitable for application scenarios that require high image quality.
Method description: Implicit key points are used as intermediate motion representations, which can effectively capture and represent the main motion features of the face, balancing generation quality and computational efficiency.
Implementation details: Implicit key points are used to extract and represent facial motion information, and animation is generated through the transformation of these key points.
Implementation details of LivePortrait
Hybrid image and video training strategy
Method description: Combine high-quality static portrait images and dynamic videos for training to enhance the generalization ability of the model, enabling it to handle portraits of various styles.
We use public datasets and our own high-quality video data for training to ensure the diversity and robustness of the model.
Upgraded network architecture
Method description: We use an advanced network architecture, including ConvNeXt-V2-Tiny as the backbone network and SPADE decoder, to improve generation quality and computational efficiency.
Implementation details: The original implicit keypoint detector, head pose estimation network and expression deformation estimation network are unified into one model to simplify the network structure and improve performance.
The SPADE decoder is used to generate high-quality animations, and the PixelShuffle layer is combined for resolution upsampling to generate clearer images.
Signature-guided implicit keypoint optimization
Method description: 2D landmarks (such as key points of eyes and lips) are introduced as guidance to optimize the learning process of implicit key points and enhance the control ability of subtle facial expressions.
Implementation details: Using 2D landmarks as supervisory signals, we optimize the locations of implicit keypoints, allowing the model to better capture micro-expressions such as blinks and eye movements.
Stitching and Reorientation Modules
Method description: A stitching module and two redirection modules (eye and lip redirection) are proposed to enhance the detail control of animation and make the generated animation more natural and smooth.
Implementation details: Stitching module: handles the seamless stitching of multiple portraits to ensure smooth transition.
Eye Redirection Module: Independently control the direction and movement of the eyes, making the eye movements in animations more realistic.
Lip Redirection Module: Precisely control the opening and closing of lips to make speech or expression changes in animation more natural.
Lip Redirection Module of LivePortrait
Efficient generation speed
Method description: The calculation process is optimized to greatly improve the generation speed and realize real-time animation generation on high-performance GPU.
Implementation details: On the RTX 4090 GPU, LivePortrait’s generation speed reaches 12.8 milliseconds per frame, enabling efficient real-time animation generation.
