We present GaussianAvatar, an efficient approach to creating realistic human
avatars with dynamic 3D appearances from a single video. We start by
introducing animatable 3D Gaussians to explicitly represent humans in various
poses and clothing styles. Such an explicit and animatable representation can
fuse 3D appearances more efficiently and consistently from 2D observations. Our
representation is further augmented with dynamic properties to support
pose-dependent appearance modeling, where a dynamic appearance network along
with an optimizable feature tensor is designed to learn the
motion-to-appearance mapping. Moreover, by leveraging the differentiable motion
condition, our method enables a joint optimization of motions and appearances
during avatar modeling, which helps to tackle the long-standing issue of
inaccurate motion estimation in monocular settings. The efficacy of
GaussianAvatar is validated on both the public dataset and our collected
dataset, demonstrating its superior performances in terms of appearance quality
and rendering efficiency.

我们提出了 GaussianAvatar，一种从单个视频中创建具有动态 3D 外观的逼真人类化身的高效方法。通过引入可动画化的 3D 高斯函数来明确表示各种姿势和服装风格的人类，可以更有效和一致地从 2D 观察中融合 3D 外观。我们的表示进一步增强了动态属性，以支持姿势相关的外观建模，其中设计了一个动态外观网络和可优化特征张量来学习运动到外观的映射。此外，通过利用可微分的运动条件，我们的方法在化身建模过程中使得动作和外观能够进行联合优化，有助于解决单眼设置中不准确的运动估计问题。GaussianAvatar 的有效性在公共数据集和我们的收集数据集上得到验证，展示了其在外观质量和渲染效率方面的优越性能。