HumanML3D Dataset

HumanML3D is a 3D human motion-language dataset that originates from a combination of HumanAct12 and Amass dataset. It covers a broad range of human actions such as daily activities (e.g., 'walking', 'jumping'), sports (e.g., 'swimming', 'playing golf'), acrobatics (e.g., 'cartwheel') and artistry (e.g., 'dancing'). Overall, HumanML3D dataset consists of 14,616 motions and 44,970 descriptions composed by 5,371 distinct words. The total length of motions amounts to 28.59 hours. The average motion length is 7.1 seconds, while average description length is 12 words.

GitHub - LinghaoChan/UniMoCap: [Open-source Project] UniMoCap: community implementation to unify the text-motion datasets (HumanML3D, KIT-ML, and BABEL) and whole-body motion dataset (Motion-X).

3D Body Keypoint Datasets — MMPose 1.3.1 documentation

GitHub - EricGuo5513/HumanML3D: HumanML3D: A large and diverse 3d human motion-language dataset.

TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts

POSTS

Congyi Wang - CatalyzeX

arxiv-sanity

arxiv-sanity

Creating Authentic Human Motion Synthesis via Diffusion - Metaphysic.ai

PDF] MotionGPT: Human Motion as a Foreign Language

MoMask: Generative Masked Modeling of 3D Human Motions – arXiv Vanity

Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition

NeurIPS 2023

Generate Movement from Text Descriptions with T2M-GPT - Voxel51

Electronics, Free Full-Text