HyperColor：一种面向游戏场景群体的自着色三维模型合成方法HyperNetwork
HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population
摘要：
设计3D游戏场景是一项繁琐的任务，通常需要大量的工作。通常，该任务涉及游戏场景中3D模型的合成、着色和放置。为了减轻这种工作量，我们可以应用机器学习来自动化游戏场景开发的某些方面。早期的研究已经解决了用机器学习自动生成游戏场景背景的问题。然而，模型自动着色仍然是一个未充分探索的问题。3D模型的自动着色是一项具有挑战性的任务，特别是在处理彩色多部分对象的数字表示时。在这种情况下，我们必须“理解”对象的组成和每个部分的着色方案。现有的单阶段方法有自己的警告，例如需要分割对象或生成必须组装在一起以产生最终模型的各个部分。我们通过提出一种两阶段训练方法来合成自动着色的3D模型来解决这些限制。在第一阶段，我们获得一个代表3D对象的3D点云，而在第二阶段，我们为这样的云中的点分配颜色。接下来，通过利用所谓的三角剖分技巧，我们生成一个3D网格，其中表面基于表示给定网格三角形顶点的着色点的插值而着色。这种方法允许我们生成一个平滑的着色方案。实验评估表明，我们的两阶段的方法给出了更好的结果，在形状重建和着色相比，传统的单阶段技术。
英文摘要：
Designing a 3D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves synthesis, coloring, and placement of 3D models within the game scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model auto-coloring remains an underexplored problem. The automatic coloring of a 3D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to ``understand'' the object's composition and coloring scheme of each part. Existing single-stage methods have their own caveats such as the need for segmentation of the object or generating individual parts that have to be assembled together to yield the final model. We address these limitations by proposing a two-stage training approach to synthesize auto-colored 3D models. In the first stage, we obtain a 3D point cloud representing a 3D object, whilst in the second stage, we assign colors to points within such cloud. Next, by leveraging the so-called triangulation trick, we generate a 3D mesh in which the surfaces are colored based on interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to generate a smooth coloring scheme. Experimental evaluation shows that our two-stage approach gives better results in terms of shape reconstruction and coloring when compared to traditional single-stage techniques.
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