# Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset

arXiv: https://arxiv.org/abs/2501.05098 · HTML: https://mlsanity.com/paper/2501.05098/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 30 of 33)

- [Pulp Motion: Framing-aware multimodal camera and human motion generation](https://mlsanity.com/paper/2510.05097/) (2025)
- [Vision Pretraining for Dense Spatial Perception](https://mlsanity.com/paper/2607.05247/) (2026)
- [SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control](https://mlsanity.com/paper/2511.19236/) (2025)
- [HumanMM: Global Human Motion Recovery from Multi-shot Videos](https://mlsanity.com/paper/2503.07597/) (2025)
- [SafeMo: Linguistically Grounded Unlearning for Trustworthy Text-to-Motion Generation](https://mlsanity.com/paper/2601.00590/) (2026)
- [ω-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation](https://mlsanity.com/paper/2608.06375/) (2026)
- [Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control](https://mlsanity.com/paper/2603.27756/) (2026)
- [Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking](https://mlsanity.com/paper/2606.03985/) (2026)
- [HyperMotionX: The Dataset and Benchmark with DiT-Based Pose-Guided Human Image Animation of Complex Motions](https://mlsanity.com/paper/2505.22977/) (2025)
- [MoSA: Motion-Coherent Human Video Generation via Structure-Appearance Decoupling](https://mlsanity.com/paper/2508.17404/) (2025)
- [Generating Human Motion Videos using a Cascaded Text-to-Video Framework](https://mlsanity.com/paper/2510.03909/) (2025)
- [Dense Motion Captioning](https://mlsanity.com/paper/2511.05369/) (2025)
- [NymeriaPlus: Enriching Nymeria Dataset with Additional Annotations and Data](https://mlsanity.com/paper/2603.18496/) (2026)
- [RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation](https://mlsanity.com/paper/2605.26241/) (2026)
- [Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control](https://mlsanity.com/paper/2602.21599/) (2026)
- [AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling](https://mlsanity.com/paper/2605.29488/) (2026)
- [Beyond MoCap: Scaling Motion Tokenizers with Synthetic Human Motion for Generative Modeling](https://mlsanity.com/paper/2606.27547/) (2026)
- [MRBench: A Comprehensive Benchmark for Human Motion-Text Retrieval](https://mlsanity.com/paper/2608.07993/) (2026)
- [HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark](https://mlsanity.com/paper/2608.13555/) (2026)
- [HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction](https://mlsanity.com/paper/2608.16222/) (2026)
- [OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data](https://mlsanity.com/paper/2506.07565/) (2025)
- [Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation](https://mlsanity.com/paper/2601.14788/) (2026)
- [Controllable Complex Human Motion Video Generation via Text-to-Skeleton Cascades](https://mlsanity.com/paper/2603.08028/) (2026)
- [HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation](https://mlsanity.com/paper/2604.25361/) (2026)
- [Fine-grained Human Motion Understanding with Language Models](https://mlsanity.com/paper/2606.20888/) (2026)
- [In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics](https://mlsanity.com/paper/2606.26981/) (2026)
- [EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal](https://mlsanity.com/paper/2607.13472/) (2026)
- [UniMoCa: Unifying Motion and Camera Controls as Visual Proxies for Faithful Human Video Generation](https://mlsanity.com/paper/2608.01944/) (2026)
- [Human-Centric Intelligence in the Era of Foundation Models: A Survey](https://mlsanity.com/paper/2608.18184/) (2026)
- [Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis](https://mlsanity.com/paper/2608.20312/) (2026)

## Abstract (arXiv)

In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++ provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments validate the accuracy of our annotation pipeline and highlight Motion-X++'s significant benefits for generating expressive, precise, and natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation,audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc.