论文标题
具有生成对抗网络的3D拓扑转换
3D Topology Transformation with Generative Adversarial Networks
论文作者
论文摘要
在过去的几年中，使用人工智能的图像和视频的产生和转变蓬勃发展。但是，只有少数作品旨在生产创意3D形状，例如雕塑。在这里，我们使用生成对抗网络（GAN）展示了一种新颖的3D到3D拓扑转换方法。我们使用称为Vox2Vox的修改后的Pix2pix GAN，在保留原始对象形状的同时，将其称为Vox2Vox。特别是，我们展示了如何将3D模型转换为两个新的体积拓扑 - 3D网络和Ghirigoro。我们描述了如何使用我们的方法来构建自定义的3D表示。我们相信产生的3D形状是新颖而鼓舞人心的。最后，我们比较了我们的方法和基线算法之间直接转换3D形状的基线算法之间的结果，而无需使用我们的gan。
Generation and transformation of images and videos using artificial intelligence have flourished over the past few years. Yet, there are only a few works aiming to produce creative 3D shapes, such as sculptures. Here we show a novel 3D-to-3D topology transformation method using Generative Adversarial Networks (GAN). We use a modified pix2pix GAN, which we call Vox2Vox, to transform the volumetric style of a 3D object while retaining the original object shape. In particular, we show how to transform 3D models into two new volumetric topologies - the 3D Network and the Ghirigoro. We describe how to use our approach to construct customized 3D representations. We believe that the generated 3D shapes are novel and inspirational. Finally, we compare the results between our approach and a baseline algorithm that directly convert the 3D shapes, without using our GAN.
