论文标题
COF：可控家具布局合成
COFS: Controllable Furniture layout Synthesis
论文作者
论文摘要
可扩展的家具布局对于虚拟现实，增强现实，游戏开发和合成数据生成的许多应用至关重要。许多现有方法将此问题作为一个序列生成问题解决，该问题对布局的元素强加了特定的订购，从而使这种方法不切实际地进行交互式编辑或场景完成。此外，大多数方法都专注于无条件生成布局，并对生成的布局提供最小的控制。我们建议COFS，这是一种基于语言建模的标准变压器体系结构块的体系结构。提出的模型是通过设计而不间断的，可以删除指定对象生成顺序的不自然要求。此外，该模型允许在多个级别上进行用户交互，从而可以对生成过程进行细粒度的控制。我们的模型始终优于通过执行定量评估来验证的其他方法。与现有方法相比，我们的方法也更快地训练和采样。
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.
