Automatic Generation of Constrained Furniture Layouts
This addresses the need for efficient and controllable content generation in virtual environments, though it is incremental as it builds on existing statistical learning and sampling techniques.
The paper tackles the problem of automatically generating furniture layouts for indoor virtual environments by learning joint statistics from training data and using conditional sampling, achieving layouts of equivalent perceived quality to training data and favorable comparison to a state-of-the-art method.
Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable. We propose a method to automatically generate furniture layouts for indoor environments. Our method is simple, efficient, human-interpretable and amenable to a wide variety of constraints. We model the composition of rooms into classes of objects and learn joint (co-occurrence) statistics from a database of training layouts. We generate new layouts by performing a sequence of conditional sampling steps, exploiting the statistics learned from the database. The generated layouts are specified as 3D object models, along with their positions and orientations. We show they are of equivalent perceived quality to the training layouts, and compare favorably to a state-of-the-art method. We incorporate constraints using a general mechanism -- rejection sampling -- which provides great flexibility at the cost of extra computation. We demonstrate the versatility of our method by applying a wide variety of constraints relevant to real-world applications.