SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments (CVPR2023)
Yudi Dai · Yitai Lin · Xiping Lin · Chenglu Wen · Lan Xu · Hongwei Yi · Siqi Shen · Yuexin Ma · Cheng Wang
- More info is coming soon…
- 05/11/2023: Released a SAM-based tool for 2D mask generation and updated the data loader example.
- 04/2023: First part of the dataset V1.0 has released! (Dataset)
- 03/2023: Initial release of the visualization Tool (SMPL-Scene Viewer) (v1.0)
- 15 sequences of 12 human subjects in
- 10 scenes in urban environments (1k – 30k
$m^2$ ) - 100k+ frames multi-source data (20 Hz)
- including 2D / 3D annotations and 3D scenes; 7 km+ human motions.
Every human subject signed permission to release their motion data for research purposes.
Num | Sequence | Traj. length ( |
Area size ( |
Frames | Motions |
---|---|---|---|---|---|
001 | campus_001 | 908 | 13,400 | 16,202 | Jogging downhill, tying shoelaces, jumping |
002 | football_002 | 221 | 200 | 4,665 | Juggling, passing, and shooting a football |
003 | street_002 | 291 | 1,600 | 6,496 | Taking photos, putting on/taking off a bag |
004 | library_001 | 440 | 2,300 | 9,949 | Borrowing books, reading, descending stairs |
005 | library_002 | 474 | 2,300 | 8,901 | Looking up, reading, returning a book |
006 | library_003 | 477 | 2,300 | 8,386 | Admiring paintings, throwing rubbish, greeting |
007 | garden_001 | 217 | 3,000 | 5,994 | Raising hand, sitting on bench, going upstairs |
008 | running_001 | 392 | 8,500 | 2,000 | Running |
009 | running_002 | 985 | 30,000 | 8,113 | Running |
010 | park_001 | 642 | 9,300 | 12,445 | Visiting a park, walking up a small hill |
011 | park_002 | 1,025 | 11,000 | 1,000 | Buying drinks, trotting, drinking |
012 | square_001 | 264 | 3,200 | 6,792 | Making phone calls, waving, drinking |
013 | sunlightRock001 | 386 | 1,900 | 10,116 | Climbing stairs, taking photos, walking |
014 | garden_002 | 209 | 4,200 | 5,604 | Stooping, crossing a bridge, sitting cross-legged |
015 | plaza_001 | 365 | 2,700 | 7,989 | Admiring sculptures, eating |
Please see processing pipeline.
Please see visualization script.
- Comparison between IMU + ICP and our optimization results.
- Comparison between original extrinsic parameters and our optimization results.
- LiDAR-based human pose estimation (HPE)
- Camera-based HPE
- Global Human Pose Estimation Comparison
The SLOPER4D codebase is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. You must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. Contact us if you are interested in commercial usage.
@InProceedings{Dai_2023_CVPR,
author = {Dai, Yudi and Lin, Yitai and Lin, Xiping and Wen, Chenglu and Xu, Lan and Yi, Hongwei and Shen, Siqi and Ma, Yuexin and Wang, Cheng},
title = {SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {682-692}
}