Today, the cutting edge of computer vision research greatly depends on the availability of large datasets, which are critical for effectively training and testing new methods. Manually annotating visual data, however, is not only a labor-intensive process but also prone to errors. In this study, we present NOVA, a versatile framework to create realistic-looking 3D rendered worlds containing procedurally generated humans with rich pixel-level ground truth annotations. NOVA can simulate various environmental factors such as weather conditions or different times of day, and bring an exceptionally diverse set of humans to life, each having a distinct body shape, gender and age. To demonstrate NOVA's capabilities, we generate two synthetic datasets for person tracking. The first one includes 108 sequences, each with different levels of difficulty like tracking in crowded scenes or at nighttime and aims for testing the limits of current state-of-the-art trackers. A second dataset of 97 sequences with normal weather conditions is used to show how our synthetic sequences can be utilized to train and boost the performance of deep-learning based trackers. Our results indicate that the synthetic data generated by NOVA represents a good proxy of the real-world and can be exploited for computer vision tasks.

中文翻译：

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NOVA：为计算机视觉任务渲染人类虚拟世界

今天，计算机视觉研究的前沿在很大程度上取决于大型数据集的可用性，这对于有效训练和测试新方法至关重要。然而，手动注释视觉数据不仅是一个劳动密集型的过程，而且容易出错。在这项研究中，我们提出了 NOVA，这是一个多功能框架，用于创建逼真的 3D 渲染世界，其中包含程序生成的具有丰富像素级地面实况注释的人类。NOVA 可以模拟各种环境因素，例如天气条件或一天中的不同时间，并将异常多样化的人类带入生活，每个人都有不同的体型、性别和年龄。为了演示 NOVA 的功能，我们生成了两个用于人员跟踪的合成数据集。第一个包含 108 个序列，每个都有不同的难度级别，例如在拥挤的场景中或夜间跟踪，旨在测试当前最先进的跟踪器的极限。具有正常天气条件的 97 个序列的第二个数据集用于展示如何利用我们的合成序列来训练和提高基于深度学习的跟踪器的性能。我们的结果表明，NOVA 生成的合成数据代表了现实世界的良好代理，可用于计算机视觉任务。