3D representation and reconstruction of human bodies have been studied for a
long time in computer vision. Traditional methods rely mostly on parametric
statistical linear models, limiting the space of possible bodies to linear
combinations. It is only recently that some approaches try to leverage neural
implicit representations for human body modeling, and while demonstrating
impressive results, they are either limited by representation capability or not
physically meaningful and controllable. In this work, we propose a novel neural
implicit representation for the human body, which is fully differentiable and
optimizable with disentangled shape and pose latent spaces. Contrary to prior
work, our representation is designed based on the kinematic model, which makes
the representation controllable for tasks like pose animation, while
simultaneously allowing the optimization of shape and pose for tasks like 3D
fitting and pose tracking. Our model can be trained and fine-tuned directly on
non-watertight raw data with well-designed losses. Experiments demonstrate the
improved 3D reconstruction performance over SoTA approaches and show the
applicability of our method to shape interpolation, model fitting, pose
tracking, and motion retargeting.

本文提出了一种基于运动学模型的神经隐式表示方法，可以同时处理人体的形状和姿态，实现了更好的三维重建效果和任务控制能力。