3D Generation

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Overview - Dataset Name: HumanRig - Paper: CVPR2025 - "HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset" - Authors: [Zedong Chu · Feng Xiong · Meiduo Liu · Jinzhi Zhang · Mingqi Shao · Zhaoxu Sun · Di Wang · Mu Xu] - The HumanRig dataset is introduced in the CVPR2025 paper titled "HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset". This work addresses the critical need for a comprehensive dataset and a robust framework for automatic rigging of 3D humanoid character models. HumanRig is a large-scale dataset of AI-generated T-pose humanoid models, all rigged with a consistent skeleton topology. It significantly surpasses previous datasets in terms of size, diversity, complexity, and practical motion applications. ### Dataset Details - Number of Samples: 11,434 - Data Splits: - Training Set: 0-9147 (80%) - Validation Set: 9148-10290 (10%) - Test Set: 10291-11433 (10%) ### Data Structure Each annotated sample in the HumanRig dataset includes the following components: - Rigged T-pose Humanoid Mesh: A rigged 3D mesh of the humanoid character in T-pose. - 3D Skeleton Joint Positions: The positions of the joints in the 3D skeleton. - Skinning Weight Matrix: A matrix that defines the influence of each joint on the vertices of the mesh. - Front-view Image: A 2D image of the humanoid character from the front view. - Camera Parameters: The parameters of the camera used to capture the front-view image. - 2D Skeleton Joint Positions: The positions of the joints in the 2D front-view image. ### Skeleton Joint Order The skeleton joints in the dataset follow a specific order: python ['Hips', 'Spine', 'Spine1', 'Spine2', 'Neck', 'Head', 'LeftShoulder', 'LeftArm', 'LeftForeArm', 'LeftHand', 'RightShoulder', 'RightArm', 'RightForeArm', 'RightHand', 'LeftUpLeg', 'LeftLeg', 'LeftFoot', 'LeftToeBase', 'RightUpLeg', 'RightLeg', 'RightFoot', 'RightToeBase'] ### Citation If you use the HumanRig dataset in your research, please cite the following paper: bibtex @article{chu2024humanrig, title={HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset}, author={Chu, Zedong and Xiong, Feng and Liu, Meiduo and Zhang, Jinzhi and Shao, Mingqi and Sun, Zhaoxu and Wang, Di and Xu, Mu}, journal={arXiv preprint arXiv:2412.02317}, year={2024} }

MeshFleet is a filtered and annotated dataset of High Quality vehicles derived from Objaverse XL. It contains the sha256 of the objects together with consitent object captions and vehicle parameters.

This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, jelly, and rigid collisions. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations. Each material contains 50 trajectories with different initial velocity field.

This dataset contains over 47,000 LEGO structures of over 28,000 unique 3D objects accompanied by detailed captions. It was used to train LegoGPT, the first approach for generating physically stable LEGO brick models from text prompts.