  We explore how to enable machines to model 3D shapes like human modelers
using deep reinforcement learning (RL). In 3D modeling software like Maya, a
modeler usually creates a mesh model in two steps: (1) approximating the shape
using a set of primitives; (2) editing the meshes of the primitives to create
detailed geometry. Inspired by such artist-based modeling, we propose a
two-step neural framework based on RL to learn 3D modeling policies. By taking
actions and collecting rewards in an interactive environment, the agents first
learn to parse a target shape into primitives and then to edit the geometry. To
effectively train the modeling agents, we introduce a novel training algorithm
that combines heuristic policy, imitation learning and reinforcement learning.
Our experiments show that the agents can learn good policies to produce regular
and structure-aware mesh models, which demonstrates the feasibility and
effectiveness of the proposed RL framework.
