examples. Given a few user-provided examples, our system can synthesize a diverse set of plausible new scenes by learning from a larger scene database. We rely on three novel contributions. First, we introduce a probabilistic model for scenes based on Bayesian networks and Gaussian mixtures that can be trained from a small number of input examples. Second, we develop a clustering algorithm that groups objects occurring in a database of scenes according to their local scene neighborhoods. These contextual categories allow the synthesis process to treat a wider variety of objects as interchangeable. Third, we train our probabilistic model on a mix of user-provided examples and relevant scenes retrieved from the database. This mixed model learning process can be controlled to introduce additional variety into the synthesized scenes. We evaluate our algorithm through qualitative results and a perceptual study in which participants judged synthesized scenes to be highly plausible, as compared to hand-created scenes.

@article{Fisher:2012:ESO,
   author = "Matthew Fisher and Daniel Ritchie and Manolis Savva and Thomas
      Funkhouser and Pat Hanrahan",
   title = "Example-based Synthesis of {3D} Object Arrangements",
   journal = "ACM Transactions on Graphics (Proc. SIGGRAPH Asia)",
   year = "2012",
   month = nov,
   volume = "31",
   number = "6"
}