Ritchie, DanielJobalia, SarahThomas, AnnaGutierrez, Diego and Sheffer, Alla2018-04-142018-04-1420181467-8659https://doi.org/10.1111/cgf.13371https://diglib.eg.org:443/handle/10.1111/cgf13371Procedural models are a powerful tool for quickly creating a variety of computer graphics content. However, authoring them is challenging, requiring both programming and artistic expertise. In this paper, we present a method for learning procedural models from a small number of example objects. We focus on the modular design setting, where objects are constructed from a common library of parts. Our procedural representation is a probabilistic program that models both the discrete, hierarchical structure of the examples as well as the continuous variability in their spatial arrangements of parts. We develop an algorithm for learning such programs from examples, using combinatorial search over program structures and variational inference to estimate continuous program parameters. We evaluate our method by demonstrating its ability to learn programs from examples of ornamental designs, spaceships, space stations, and castles. Experiments suggest that our learned programs can reliably generate a variety of new objects that are perceptually indistinguishable from hand-crafted examples.Computing methodologiesProbabilistic reasoningNeural networksShape analysis10.1111/cgf.13371401-413