Order-Aware Generative Modeling Using the 3D-Craft Dataset

Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, Charles R. Qi, Shubham Tulsiani, Arthur Szlam, C. Lawrence Zitnick; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1764-1773

In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available.