Deep 3D Modeling of Human Bodies from Freehand Sketching

Creating high-quality 3D human body models by freehand sketching is challenging because of the sparsity and ambiguity of hand-drawn strokes. In this paper, we present a sketch-based modeling system for human bodies using deep neural networks. Considering the large variety of human body shapes and poses, we adopt the widely-used parametric representation, SMPL, to produce high-quality models that are compatible with many further applications, such as telepresence, game production, and so on. However, precisely mapping hand-drawn sketches to the SMPL parameters is non-trivial due to the non-linearity and dependency between articulated body parts. In order to solve the huge ambiguity in mapping sketches onto the manifold of human bodies, we introduce the skeleton as the intermediate representation. Our skeleton-aware modeling network first interprets sparse joints from coarse sketches and then predicts the SMPL parameters based on joint-wise features. This skeleton-aware intermediate representation effectively reduces the ambiguity and complexity between the two high-dimensional spaces. Based on our light-weight interpretation network, our system supports interactive creation and editing of 3D human body models by freehand sketching.