Human motion prediction

EMDB contains in-the-wild videos of human activity recorded with a hand-held iPhone. It features reference SMPL body pose and shape parameters, as well as global body root and camera trajectories. The reference 3D poses were obtained by jointly fitting SMPL to 12 body-worn electromagnetic sensors and image data. For the latter we fit a neural implicit avatar model to allow for a dense pixel-wise fitting objective. EMDB contains: * 81 sequences * 105 000 frames * 10 actors (5 female, 5 male) * Global camera trajectories * SMPL pose and shape parameters * 2D Keypoints The dataset can be used to evaluate the following tasks: * Camera-relative 3D human pose and shape estimation from monocular videos. * Global 3D human pose and shape estimation including camera trajectories from monocular videos. * Human motion prediction.

The GTA Indoor Motion dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. It consists of HD RGB-D image sequences of 3D human motion from a realistic game engine. The dataset has clean 3D human pose and camera pose annotations, and large diversity in human appearances, indoor environments, camera views, and human activities. Source: https://github.com/ZheC/GTA-IM-Dataset Image Source: https://github.com/ZheC/GTA-IM-Dataset

The MoCapAct dataset contains training data and models for humanoid locomotion research. It consists of expert policies that are trained to track individual clip snippets and HDF5 files of noisy rollouts collected from each expert, including proprioceptive observations and actions.

Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. Source: VIENA2: A Driving Anticipation Dataset

The NuiSI dataset contains skeleton tracking trajectories of Human Interaction Partners performing a variety of physically interactive behaviors (waving, handshaking, rocket fistbump, parachute fistbump) with each other. This is inspired by the dataset in Bütepage et al. "Imitating by generating: Deep generative models for imitation of interactive tasks." Frontiers in Robotics and AI (2020) wherein they capture a dataset with rokoko motion capture suits. Instead we track the skeletons of the interaction partner with Intel Realsense cameras using Nuitrack, for a more realistic scenario, with noise coming from the depth sensor, the skeleton tracking and some partial occlusions. This makes it more representative of real world interactions with a Robot equipped with an RGBD camera. T This dataset is used in our papers for training Interaction models for Human-Robot Interaction with a humanoid social robot. If you find the dataset useful in your work, please cite our paper: Prasad, V., Heitlinger, L., Koert, D., Stock-Homburg, R., Peters, J., & Chalvatzaki, G. (2023). Learning multimodal latent dynamics for human-robot interaction. arXiv preprint arXiv:2311.16380. @article{prasad2023learning, title={Learning multimodal latent dynamics for human-robot interaction}, author={Prasad, Vignesh and Heitlinger, Lea and Koert, Dorothea and Stock-Homburg, Ruth and Peters, Jan and Chalvatzaki, Georgia}, journal={arXiv preprint arXiv:2311.16380}, year={2023} }