To enable realistic experience in AR/VR and digital entertainment, we present
the first point-based human avatar model that embodies the entirety expressive
range of digital humans. We employ two MLPs to model pose-dependent deformation
and linear skinning (LBS) weights. The representation of appearance relies on a
decoder and the features that attached to each point. In contrast to
alternative implicit approaches, the oriented points representation not only
provides a more intuitive way to model human avatar animation but also
significantly reduces both training and inference time. Moreover, we propose a
novel method to transfer semantic information from the SMPL-X model to the
points, which enables to better understand human body movements. By leveraging
the semantic information of points, we can facilitate virtual try-on and human
avatar composition through exchanging the points of same category across
different subjects. Experimental results demonstrate the efficacy of our
presented method.

为了实现真实的 AR/VR 和数字娱乐体验，我们提出了第一个点云人物模型，它涵盖了数字人物的全部表达范围。我们使用两个多层感知器来建模姿势相关的变形和线性蒙皮权重。外观的表示依赖于解码器和附加到每个点的特征。与替代的隐式方法相比，朝向点的表示不仅提供了更直观的方式来建模人物动画，还显著减少了训练和推理时间。此外，我们提出了一种新的方法将语义信息从 SMPL-X 模型转移到点云中，从而更好地理解人体运动。通过利用点的语义信息，我们可以通过在不同主体之间交换相同类别的点来促进虚拟试穿和人物组合。实验结果证明了我们提出方法的效果。