Abstract
In this work, we address a novel and practical problem of automatically generating a room design from given room function and basic geometry, which can be described as picking appropriate objects from a given database, and placing the objects with a group of pre-defined criteria. We formulate both object selection and placement problems as probabilistic models. The object selection is first formulated as a supervised generative model, to take room function into consideration. Object placement problem is then formulated as a Bayesian model, where parameters are inferred with Maximizing a Posteriori (MAP) objective. By introducing a solver based on Markov Chain Monte Carlo (MCMC), the placement problem is solved efficiently.
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Acknowledgments
This work was supported by Research Grant of Beijing Higher Institution Engineering Research Center and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n\(^{\circ }\) [612627].
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Liang, Y., Zhang, SH., Martin, R.R. (2017). Automatic Data-Driven Room Design Generation. In: Chang, J., Zhang, J., Magnenat Thalmann, N., Hu, SM., Tong, R., Wang, W. (eds) Next Generation Computer Animation Techniques. AniNex 2017. Lecture Notes in Computer Science(), vol 10582. Springer, Cham. https://doi.org/10.1007/978-3-319-69487-0_10
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