论文标题

场景布局的端到端优化

End-to-End Optimization of Scene Layout

论文作者

Luo, Andrew, Zhang, Zhoutong, Wu, Jiajun, Tenenbaum, Joshua B.

论文摘要

我们为场景图表的场景布局综合提出了一个端到端的变分生成模型。与无条件的场景布局生成不同,我们使用场景图作为抽象但一般表示形式,以指导各种场景布局的综合,这些布局满足了场景图中包含的关系。这会产生对合成过程的更灵活的控制,从而允许各种形式的输入,例如从句子中提取的场景布局或从单个颜色图像中推断出来。使用条件布局合成器,我们可以生成各种布局,这些布局共享输入示例的相同结构。除了这种有条件的生成设计外,我们还集成了一个可区分的渲染模块,该模块仅使用场景的2D投影启用布局改进。给定深度和语义图,可区分的渲染模块可以优化综合布局,以按分析方式拟合给定输入。实验表明,我们的模型在条件场景合成中实现了更高的准确性和多样性,并允许从各种输入形式产生基于示例的场景。

We propose an end-to-end variational generative model for scene layout synthesis conditioned on scene graphs. Unlike unconditional scene layout generation, we use scene graphs as an abstract but general representation to guide the synthesis of diverse scene layouts that satisfy relationships included in the scene graph. This gives rise to more flexible control over the synthesis process, allowing various forms of inputs such as scene layouts extracted from sentences or inferred from a single color image. Using our conditional layout synthesizer, we can generate various layouts that share the same structure of the input example. In addition to this conditional generation design, we also integrate a differentiable rendering module that enables layout refinement using only 2D projections of the scene. Given a depth and a semantics map, the differentiable rendering module enables optimizing over the synthesized layout to fit the given input in an analysis-by-synthesis fashion. Experiments suggest that our model achieves higher accuracy and diversity in conditional scene synthesis and allows exemplar-based scene generation from various input forms.

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