We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. To achieve this, we propose a part-aware learned forward skinning module that can be driven by the parameter space of SMPL-X, allowing for expressive animation of X-Avatars. To efficiently learn the neural shape and deformation fields, we propose novel part-aware sampling and initialization strategies. This leads to higher fidelity results, especially for smaller body parts while maintaining efficient training despite increased number of articulated bones. To capture the appearance of the avatar with high-frequency details, we extend the geometry and deformation fields with a texture network that is conditioned on pose, facial expression, geometry and the normals of the deformed surface. We show experimentally that our method outperforms strong baselines in both data domains both quantitatively and qualitatively on the animation task. To facilitate future research on expressive avatars we contribute a new dataset, called X-Humans, containing 233 sequences of high-quality textured scans from 20 participants, totalling 35,500 data frames.

我们展示的是X-阿凡达，一种新颖的阿凡达模型，它捕捉到了 数字人类的表现力将带来逼真的体验 网真、AR/VR等。我们的方法是为身体、手、面部建模 整体的表情和外表，可以从以下两种方式中学习 全3D扫描或RGB-D数据。为了实现这一点，我们提出了一种部分意识的学习 可由SMPL-X的参数空间驱动的正向蒙皮模块， 允许X-头像的富有表现力的动画。要有效地学习神经 形状和变形场，我们提出了一种新的局部感知采样和 初始化策略。这会带来更高的保真度结果，尤其是 对于较小的身体部位，同时保持有效的训练，尽管增加了 关节骨骼的数量。用来捕捉化身的外观 高频细节，我们用一个 纹理网络，以姿势、面部表情、几何和 变形曲面的法线。我们通过实验证明了我们的方法 在数量和性能上都超过了两个数据领域的强大基线 在动画任务上定性。为了促进未来对 表现力化身我们贡献了一个新的数据集，称为X-人类，包含233个 来自20名参与者的高质量纹理扫描序列，总计35,500 数据帧。