Visualization, Adaptation, and Transformation of Procedural Grammars

Procedural shape grammars are powerful tools for the automatic generation of highly detailed 3D content from a set of descriptive rules. It is easy to encode variations in stochastic and parametric grammars, and an uncountable number of models can be generated quickly. While shape grammars offer these advantages over manual 3D modeling, they also suffer from certain drawbacks. We present three novel methods that address some of the limitations of shape grammars. First, it is often difficult to grasp the diversity of models defined by a given grammar. We propose a pipeline to automatically generate, cluster, and select a set of representative preview images for a grammar. The system is based on a new view attribute descriptor that measures how suitable an image is in representing a model and that enables the comparison of different models derived from the same grammar. Second, the default distribution of models in a stochastic grammar is often undesirable. We introduce a framework that allows users to design a new probability distribution for a grammar without editing the rules. Gaussian process regression interpolates user preferences from a set of scored models over an entire shape space. A symbol split operation enables the adaptation of the grammar to generate models according to the learned distribution. Third, it is hard to combine elements of two grammars to emerge new designs. We present design transformations and grammar co-derivation to create new designs from existing ones. Algorithms for fine-grained rule merging can generate a large space of design variations and can be used to create animated transformation sequences between different procedural designs. Our contributions to visualize, adapt, and transform grammars makes the procedural modeling methodology more accessible to non-programmers

Deformation-Aware Split Grammars for Architectural Models

With the current state of video games growing in scale, manual content creation may no longer be feasible in the future. Split grammars are a promising technology for large scale procedural generation of urban structures, which are very common in video games. Buildings with curved parts, however, can currently only be approximated by static premodeled assets, and rules apply only to planar surface parts. We present an extension to current split grammar systems that allows the generation of curved architecture through freeform deformations that can be introduced at any level in a grammar. Further subdivision rules can then adapt to these deformations to maintain length constraints, and repetitions can adjust to more or less space