3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915193368346624 |
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| author | Corral-Soto, Eduardo R. Liu, Yang Cao, Tongtong Ren, Yuan Bingbing, Liu |
| author_facet | Corral-Soto, Eduardo R. Liu, Yang Cao, Tongtong Ren, Yuan Bingbing, Liu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10782 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications Corral-Soto, Eduardo R. Liu, Yang Cao, Tongtong Ren, Yuan Bingbing, Liu Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | 3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.10782 |