It is a challenging task to transform style of 3D shapes for generating diverse outputs with learning-based methods. The reasons include two folds: (1) the lack of training data with different styles and (2) multi-modal information of 3D shapes which are hard to disentangle. In this work, a multi-view-based neural network model is proposed to learn style transformation while preserving contents of 3D shapes from unpaired domains. Given two sets of shapes in different style domains, such as Japanese chairs and Ming chairs, multi-view representations of each shape are calculated, and style transformation between these two sets is learnt based on these representations. This multi-view representation not only preserves the structural details of a 3D shape, but also ensures the richness of the training data. At test stage, transformed maps are generated with the trained network by the combination of the extracted style/content features from multi-view representation and new style features. Then, transformed maps are consolidated into a 3D point cloud by solving a domain-stability optimization problem. Depth maps from all viewpoints are fused to obtain a shape whose style is similar to the target shape. Experimental results demonstrate that the proposed method outperforms the baselines and state-of-the-art approaches on style transformation.

中文翻译：

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多视图 3D 形状样式转换

使用基于学习的方法转换 3D 形状的样式以生成不同的输出是一项具有挑战性的任务。原因包括两个方面：(1) 缺乏不同风格的训练数据；(2) 难以解开的 3D 形状的多模态信息。在这项工作中，提出了一种基于多视图的神经网络模型来学习样式转换，同时保留来自未配对域的 3D 形状的内容。给定两组不同风格领域的形状，例如日式椅子和明式椅子，计算每个形状的多视图表示，并基于这些表示学习这两组之间的风格转换。这种多视图表示不仅保留了 3D 形状的结构细节，还确保了训练数据的丰富性。在测试阶段，通过从多视图表示中提取的样式/内容特征和新样式特征的组合，使用经过训练的网络生成转换后的地图。然后，通过解决域稳定性优化问题，将转换后的地图合并为 3D 点云。融合所有视点的深度图以获得与目标形状相似的形状。实验结果表明，所提出的方法在风格转换方面优于基线和最先进的方法。融合所有视点的深度图以获得与目标形状相似的形状。实验结果表明，所提出的方法在风格转换方面优于基线和最先进的方法。融合所有视点的深度图以获得与目标形状相似的形状。实验结果表明，所提出的方法在风格转换方面优于基线和最先进的方法。