We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate Physically plausible Upper Body Animation (PUBA) using interaction in the image space, such as dragging their hand to various locations. We formulate a reinforcement learning problem to train a dynamic model that predicts the person's next 2D state (i.e., keypoints on the image) conditioned on a 3D action (i.e., joint torque), and a policy that outputs optimal actions to control the person to achieve desired goals. The dynamic model leverages the expressiveness of 3D simulation and the visual realism of 2D videos. PUBA generates 2D keypoint sequences that achieve task goals while being responsive to forceful perturbation. The sequences of keypoints are then translated by a pose-to-image generator to produce the final photorealistic video.

提出了一种新的方法，用于生成可控，动态响应和逼真的人物动画，该算法利用基于图像的交互生成基于物理的人体上半身动画（Physically Plausible Upper Body Animation，PUBA）并利用强化学习训练动态模型以预测人的下一个2D状态，并输出最佳动作来控制人达到所需目标，并且可以生成响应力量干扰的2D关键点序列并最终产生逼真的视频。

从单张图片中生成人类上半身的物理可信动画