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：近年来，合成数据生成与逼真渲染技术在目标跟踪和人体姿态估计等任务中取得了显著进展。然而，大多数机器人应用的仿真环境通常局限于（半）静态场景，且采用特定传感器和低视觉保真度。为解决这一问题，本文提出了一种完全可定制框架GRADE（生成逼真动态环境），该框架首次在文献[1]中提出，专为机器人研究设计。GRADE支持完整的仿真控制、ROS集成、真实物理引擎，同时能生成高视觉保真度的图像和真值数据。我们利用GRADE生成一个以室内动态场景（包含人和飞行物体）为核心的数据集，并基于此评估了YOLO和Mask R-CNN在人体分割与检测任务中的性能。实验结果表明，将GRADE生成的数据用于预训练阶段可提升模型性能。此外，即使仅使用合成数据进行训练，模型也能在TUM-RGBD等同类应用领域的真实图像上实现良好的泛化能力。相关代码、结果、预训练模型及生成数据均已在https://eliabntt.github.io/grade-rr 开源提供。