3DArticCyclists：为计算机视觉应用生成合成的可关节化8自由度姿态可控骑行者数据
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications
AI总结：
本文提出3DArticCyclists框架，通过3DArticBikes数据集、3DGS重建渲染、参数化8自由度自行车组合模型及逆向运动学姿态细化，生成姿态可控的合成动态3D骑行者数据，以缓解自动驾驶中骑行者训练数据稀缺问题。
摘要：
在自动驾驶感知中，骑行者被视为安全关键场景对象。常用的公开自动驾驶数据集通常包含大量汽车和车辆对象实例，但骑行者实例数量较少，且外观和姿态多样性通常有限。这种骑行者训练数据稀缺问题不仅限制了深度学习感知模型在骑行者语义分割、姿态估计和骑行者过街意图预测方面的泛化能力，也限制了新的骑行者相关任务的研究，例如细粒度骑行者姿态估计以及人与关节化物体之间复杂交互下的时空分析。为了解决这一数据稀缺问题，本文提出一个框架，用于生成合成的动态3D骑行者数据资产，这些资产可用于为不同任务生成训练数据。在我们的框架中，我们设计了一种方法，用于创建一个新的基于部件的多视角关节化合成3D自行车数据集，我们称之为3DArticBikes，并用其训练基于3D高斯泼溅（3DGS）的重建与图像渲染方法。随后，我们提出一个参数化自行车3DGS组合模型，以组装8自由度姿态可控的3D自行车。最后，利用骑行者视频中的动态信息，我们通过重新摆姿一个可选择的合成3D人物来构建完整的合成动态3D骑行者（骑行者踩踏自行车），同时使用所提出的基于3D关键点优化的逆向运动学姿态细化方法，将骑行者自动放置到我们新的关节化3D自行车之一上。我们给出了定性和定量结果，并将我们生成的骑行者与近期基于稳定扩散方法生成的骑行者进行比较。
英文摘要：
In Autonomous Driving (AD) Perception, cyclists are considered safety-critical scene objects. Commonly used publicly-available AD datasets typically contain large amounts of car and vehicle object instances but a low number of cyclist instances, usually with limited appearance and pose diversity. This cyclist training data scarcity problem not only limits the generalization of deep-learning perception models for cyclist semantic segmentation, pose estimation, and cyclist crossing intention prediction, but also limits research on new cyclist-related tasks such as fine-grained cyclist pose estimation and spatio-temporal analysis under complex interactions between humans and articulated objects. To address this data scarcity problem, in this paper we propose a framework to generate synthetic dynamic 3D cyclist data assets that can be used to generate training data for different tasks. In our framework, we designed a methodology for creating a new part-based multi-view articulated synthetic 3D bicycle dataset that we call 3DArticBikes that we use to train a 3D Gaussian Splatting (3DGS)-based reconstruction and image rendering method. We then propose a parametric bicycle 3DGS composition model to assemble 8-DoF pose-controllable 3D bicycles. Finally, using dynamic information from cyclist videos, we build a complete synthetic dynamic 3D cyclist (rider pedaling a bicycle) by re-posing a selectable synthetic 3D person, while automatically placing the rider onto one of our new articulated 3D bicycles using a proposed 3D Keypoint optimization-based Inverse Kinematics pose refinement. We present both, qualitative and quantitative results where we compare our generated cyclists against those from a recent stable diffusion-based method.
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