COFS: Controllable Furniture layout Synthesis
This addresses the need for scalable and controllable furniture layout synthesis in domains such as virtual reality and game development, offering an incremental improvement over prior methods by removing ordering constraints and enhancing interactivity.
The paper tackles the problem of generating furniture layouts for applications like VR and game development by proposing COFS, a transformer-based model that is invariant to object order and allows fine-grained user control, achieving superior performance and faster training/sampling compared to existing methods.
Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation. Many existing methods tackle this problem as a sequence generation problem which imposes a specific ordering on the elements of the layout making such methods impractical for interactive editing or scene completion. Additionally, most methods focus on generating layouts unconditionally and offer minimal control over the generated layouts. We propose COFS, an architecture based on standard transformer architecture blocks from language modeling. The proposed model is invariant to object order by design, removing the unnatural requirement of specifying an object generation order. Furthermore, the model allows for user interaction at multiple levels enabling fine grained control over the generation process. Our model consistently outperforms other methods which we verify by performing quantitative evaluations. Our method is also faster to train and sample from, compared to existing methods.