Klaus Greff¹, Francois Belletti¹, Lucas Beyer¹, Carl Doersch⁶, Yilun Du⁵, Daniel Duckworth¹, David J Fleet^1,2, Dan Gnanapragasam¹, Florian Golemo^4,9, Charles Herrmann¹, Thomas Kipf¹, Abhijit Kundu¹, Dmitry Lagun¹, Issam Laradji^3,9, Hsueh-Ti (Derek) Liu², Henning Meyer¹, Yishu Miao¹⁰, Derek Nowrouzezahrai^3,4, Cengiz Öztireli^1,8, Etienne Pot¹, Mehdi S. M. Sajjadi¹, Matan Sela¹, Noha Radwan¹, Daniel Rebain^1,7, Sara Sabour^1,2, Vincent Sitzmann⁵, Austin Stone¹, Deqing Sun¹, Suhani Vora¹, Ziyu Wang¹⁰, Tianhao Wu⁸, Kwang Moo Yi⁷, Fangcheng Zhong⁸, Andrea Tagliasacchi^1,2,11
1Google ²University of Toronto ³McGill University ⁴Mila ⁵MIT ⁶DeepMind
⁷UBC ⁸University of Cambridge ⁹ServiceNow ¹⁰Haiper ¹¹Simon Fraser University

Data is the driving force of machine learning, with the amount and quality of training data often being more impor- tant for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can cir- cumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture de- sign and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.

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