Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited use for the synthesis of 3D shapes so far. This is mainly due to the lack of a straightforward way to linearize 3D data as well as to scaling problems with the length of the resulting sequences when describing complex shapes. In this work we address both of these problems. We use octrees as a compact hierarchical shape representation that can be sequentialized by traversal ordering. Moreover, we introduce an adaptive compression scheme, that significantly reduces sequence lengths and thus enables their effective generation with a transformer, while still allowing fully autoregressive sampling and parallel training. We demonstrate the performance of our model by comparing against the state-of-the-art in shape generation.

翻译：事实证明,在NLP的文本生成任务中,自动递减模型非常强大,最近也越来越受到图像生成的欢迎,然而,迄今为止,它们对于三维形状合成的用途有限,这主要是由于缺乏直线化三维数据以及描述复杂形状时所产生序列长度问题的规模问题。在这项工作中,我们处理这两个问题。我们使用奥氏树作为紧凑的等级形状,可以通过轮廓顺序顺序排列。此外,我们引入了适应性压缩计划,大大缩短序列长度,从而能够以变压器有效生成3D形状,同时仍然允许完全自动递增抽样和平行培训。我们通过比较形状生成中的最新技术来展示我们的模型的性能。