Inferring the 3D pose of a character from a drawing is a complex and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualization. In this article, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The inference of the 3D pose is formulated as an optimization problem and a parallel variation of the Particle Swarm Optimization algorithm called PARAC-LOAPSO is utilized for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp, a horse, and a human character. The results show that this method is robust, highly scalable, and able to be extended to various types of models.