{"url":"/dataset/motion-x-1","name":"Motion-X++","full_name":null,"description_markdown":"In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing\r\nmotion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions,\r\nand are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue,\r\nwe develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural\r\nlabels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into\r\nMotion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++\r\nprovides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K\r\naudios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments\r\nvalidate the accuracy of our annotation pipeline and highlight Motion-X++’s significant benefits for generating expressive, precise, and\r\nnatural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation,\r\naudio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc.","description_withheld":null,"homepage":"","introduced_date":"2025-01-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/motion-x-a-large-scale-multimodal-3d-whole","title":"Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset","first_author":"Yuhong Zhang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Motion-X++"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}