Dang, MinhLienhard, StefanCeylan, DuyguNeubert, BorisWonka, PeterPauly, Mark2015-12-022015-12-022015-12-02201510.1145/2816795.2818069https://infoscience.epfl.ch/handle/20.500.14299/121018WOS:000363671200043A shape grammar defines a procedural shape space containing a variety of models of the same class, e.g. buildings, trees, furniture, airplanes, bikes, etc. We present a framework that enables a user to interactively design a probability density function (pdf) over such a shape space and to sample models according to the designed pdf. First, we propose a user interface that enables a user to quickly provide preference scores for selected shapes and suggest sampling strategies to decide which models to present to the user to evaluate. Second, we propose a novel kernel function to encode the similarity between two procedural models. Third, we propose a framework to interpolate user preference scores by combining multiple techniques: function factorization, Gaussian process regression, auto-relevance detection, and l(1) regularization. Fourth, we modify the original grammars to generate models with a pdf proportional to the user preference scores. Finally, we provide evaluations of our user interface and framework parameters and a comparison to other exploratory modeling techniques using modeling tasks in five example shape spaces: furniture, low-rise buildings, skyscrapers, airplanes, and vegetation.procedural modelinginteractive designregressionpreference elicitationactive learningtext::conference output::conference proceedings::conference paper