Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. Simulations for most robotics applications are obtained in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we present a fully customizable framework for generating realistic animated dynamic environments (GRADE) for robotics research, first introduced in [1]. GRADE supports full simulation control, ROS integration, realistic physics, while being in an engine that produces high visual fidelity images and ground truth data. We use GRADE to generate a dataset focused on indoor dynamic scenes with people and flying objects. Using this, we evaluate the performance of YOLO and Mask R-CNN on the tasks of segmenting and detecting people. Our results provide evidence that using data generated with GRADE can improve the model performance when used for a pre-training step. We also show that, even training using only synthetic data, can generalize well to real-world images in the same application domain such as the ones from the TUM-RGBD dataset. The code, results, trained models, and the generated data are provided as open-source at https://eliabntt.github.io/grade-rr.

翻译：最近，合成数据生成与逼真渲染技术推动了目标跟踪和人体姿态估计等任务的发展。然而，大多数机器人应用领域的仿真环境存在以下局限：场景（半）静态、传感器类型固定、视觉保真度低。为解决这一问题，我们提出了一种完全可定制的框架——GRADE（生成逼真动态环境的可配置系统，该框架首次在文献[1]中提出），专用于机器人研究。GRADE支持完整的仿真控制、ROS集成、真实物理引擎，同时可在产生高视觉保真度图像和真值数据的引擎中运行。我们利用GRADE生成了一个专注于室内动态场景（含人物和飞行物体）的数据集。在此基础上，评估了YOLO和Mask R-CNN在执行人体分割与检测任务时的性能。实验结果表明，使用GRADE生成的数据进行预训练可提升模型性能。同时，即使仅使用合成数据训练，模型也能在相同应用领域（如TUM-RGBD数据集中的真实图像）中实现良好的泛化能力。相关代码、结果、训练模型及生成数据已开源发布于https://eliabntt.github.io/grade-rr。