Portrait Animation aims to synthesize a lifelike video from a single source image, using it as an appearance reference, with motion (i.e., facial expressions and head pose) derived from a driving video, audio, text, or generation. Instead of following mainstream diffusion-based methods, we explore and extend the potential of the implicit-keypoint-based framework, which effectively balances computational efficiency and controllability. Building upon this, we develop a video-driven portrait animation framework named LivePortrait with a focus on better generalization, controllability, and efficiency for practical usage. To enhance the generation quality and generalization ability, we scale up the training data to about 69 million high-quality frames, adopt a mixed image-video training strategy, upgrade the network architecture, and design better motion transformation and optimization objectives. Additionally, we discover that compact implicit keypoints can effectively represent a kind of blendshapes and meticulously propose a stitching and two retargeting modules, which utilize a small MLP with negligible computational overhead, to enhance the controllability. Experimental results demonstrate the efficacy of our framework even compared to diffusion-based methods. The generation speed remarkably reaches 12.8ms on an RTX 4090 GPU with PyTorch. The inference code and models are available at https://github.com/KwaiVGI/LivePortrait

从单一源图像中提取逼真的视频合成为目标，采用驱动视频、音频、文本或生成的运动为依据；通过探索和扩展基于隐式关键点的框架的潜力，建立了一种名为LivePortrait的视频驱动肖像动画框架，重点关注更好的泛化性、可控性和效率以适应实际使用；实验证明了该框架的有效性，甚至优于传播方法；在RTX 4090 GPU上使用PyTorch，生成速度显著达到12.8毫秒。

LivePortrait：高效的人像动画拼接和重定向控制