Embodied AI Datasets

Massive 3D Models & Scenes Dataset includes 270 million sets of 3D models and 18 million 3D scenes. 3D models cover conventional models, interactive models, and physics-enhanced models with various objects in indoor residential environments. 3D scenes cover indoor home decoration scenarios and commercial space environments. This dataset can be used for tasks like 3D asset generation, virtual environment simulation, AI model training, and industrial design applications.

This dataset contains 116,048 sets of 3D handpose data, each set includes hand mask image(RGB, 24-bit), depth image(16-bit), camera intrinsic parameter file(TXT), 3D keypoints file(OBJ), mesh file(OBJ), gesture type file(TXT), keypoints demo image(JPG), and mesh demo image(JPG). The data is collected indoors, with the right hand (no handheld objects), covering both first-person and third-person perspectives, multiple gesture types, finger poses, hand overall rotation poses, individuals and Kinect devices used. This dataset does not include personally identifiable facial information, with hand mask images and depth images aligned. This dataset can be used for tasks such as handpose recognition, hand 3D reconstruction, and hand keypoints detection.

This large-scale egocentric dataset includes over 100,000 hours of human manipulation data captured from a first-person perspective across diverse real-world scenarios. Each data sample includes synchronized multimodal information: binocular video, camera parameters, 3D scene reconstruction point clouds, human joint tracking, and stepwise semantic annotations. It is designed to train embodied AI and Vision-Language-Action (VLA) models, enabling systems to learn from human demonstrations in realistic environments.