HumanML3D Dataset
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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.
arxiv-sanity
GitHub - Mathux/AMASS-Annotation-Unifier: Unify text-motion datasets (like BABEL, HumanML3D, KIT-ML) into a common motion-text representation.
Top Important Computer Vision Papers for the Week from 27/11 to 03/12, by Youssef Hosni
PDF] Human Motion Diffusion Model
J. Imaging, Free Full-Text
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MoMask: Generative Masked Modeling of 3D Human Motions – arXiv Vanity
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
POSTS
NeurIPS 2023
Percentage of relevant and irrelevant descriptions retrieved at rank 1
Congyi Wang - CatalyzeX