  Designing a 3D game scene is a tedious task that often requires a substantial
amount of work. Typically, this task involves synthesis, coloring, and
placement of 3D models within the game scene. To lessen this workload, we can
apply machine learning to automate some aspects of the game scene development.
Earlier research has already tackled automated generation of the game scene
background with machine learning. However, model auto-coloring remains an
underexplored problem. The automatic coloring of a 3D model is a challenging
task, especially when dealing with the digital representation of a colorful,
multipart object. In such a case, we have to ``understand'' the object's
composition and coloring scheme of each part. Existing single-stage methods
have their own caveats such as the need for segmentation of the object or
generating individual parts that have to be assembled together to yield the
final model. We address these limitations by proposing a two-stage training
approach to synthesize auto-colored 3D models. In the first stage, we obtain a
3D point cloud representing a 3D object, whilst in the second stage, we assign
colors to points within such cloud. Next, by leveraging the so-called
triangulation trick, we generate a 3D mesh in which the surfaces are colored
based on interpolation of colored points representing vertices of a given mesh
triangle. This approach allows us to generate a smooth coloring scheme.
Experimental evaluation shows that our two-stage approach gives better results
in terms of shape reconstruction and coloring when compared to traditional
single-stage techniques.
