Neural radiance fields are capable of reconstructing high-quality drivable human avatars but are expensive to train and render. To reduce consumption, we propose Animatable 3D Gaussian, which learns human avatars from input images and poses. We extend 3D Gaussians to dynamic human scenes by modeling a set of skinned 3D Gaussians and a corresponding skeleton in canonical space and deforming 3D Gaussians to posed space according to the input poses. We introduce hash-encoded shape and appearance to speed up training and propose time-dependent ambient occlusion to achieve high-quality reconstructions in scenes containing complex motions and dynamic shadows. On both novel view synthesis and novel pose synthesis tasks, our method outperforms existing methods in terms of training time, rendering speed, and reconstruction quality. Our method can be easily extended to multi-human scenes and achieve comparable novel view synthesis results on a scene with ten people in only 25 seconds of training.
神经辐射场能够重建高质量的可驱动人类化身，但训练和渲染成本高昂。为了减少消耗，我们提出了可动画的3D高斯方法，它从输入图像和姿势中学习人类化身。我们通过建模一组被蒙皮的3D高斯和相应的骨骼在标准空间中，根据输入的姿势将3D高斯变形到姿势空间，将3D高斯扩展到动态人类场景。我们引入哈希编码的形状和外观来加速训练，并提出时间依赖的环境遮挡，以实现在包含复杂运动和动态阴影的场景中高质量的重建。在新视角合成和新姿势合成任务上，我们的方法在训练时间、渲染速度和重建质量方面均优于现有方法。我们的方法可以轻松扩展到多人场景，并在只有25秒的训练中实现十人场景的可比新视角合成结果。