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.

翻译：我们提出GaussianAvatar，一种从单段视频高效创建具有动态3D外观的逼真人体化身方法。首先引入可动画化3D高斯来显式表示处于不同姿态和服装风格下的人体。这种显式且可动画化的表示能从2D观测中更高效一致地融合3D外观。我们进一步通过动态属性增强该表示以支持姿态相关外观建模，其中设计了动态外观网络与可优化特征张量来学习运动到外观的映射。此外，通过利用可微运动条件，本方法可在化身建模过程中联合优化运动与外观，这有助于解决单目设置中长期存在的运动估计不准确问题。在公开数据集和自采数据集上验证了GaussianAvatar的有效性，展示了其在外观质量和渲染效率方面的优越性能。