# CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions

arXiv: https://arxiv.org/abs/2303.17948 · HTML: https://mlsanity.com/paper/2303.17948/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 80 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 30 of 46)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [PoseTrack: A Benchmark for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1710.10000/) (2017)
- [Populating 3D Scenes by Learning Human-Scene Interaction](https://mlsanity.com/paper/2012.11581/) (2020)
- [DeepCap: Monocular Human Performance Capture Using Weak Supervision](https://mlsanity.com/paper/2003.08325/) (2020)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices](https://mlsanity.com/paper/2112.07642/) (2021)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs](https://mlsanity.com/paper/1703.08014/) (2017)
- [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022)
- [Learning Motion Priors for 4D Human Body Capture in 3D Scenes](https://mlsanity.com/paper/2108.10399/) (2021)

## Built on by (most cited first, 27 of 27)

- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement](https://mlsanity.com/paper/2406.19353/) (2024)
- [Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation](https://mlsanity.com/paper/2312.07063/) (2023)
- [LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment](https://mlsanity.com/paper/2403.13307/) (2024)
- [Joint Optimization for 4D Human-Scene Reconstruction in the Wild](https://mlsanity.com/paper/2501.02158/) (2025)
- [LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment](https://mlsanity.com/paper/2402.17171/) (2024)
- [MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors](https://mlsanity.com/paper/2403.17610/) (2024)
- [Synergistic Global-space Camera and Human Reconstruction from Videos](https://mlsanity.com/paper/2405.14855/) (2024)
- [RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method](https://mlsanity.com/paper/2403.19501/) (2024)
- [Motion Synthesis with Sparse and Flexible Keyjoint Control](https://mlsanity.com/paper/2503.15557/) (2025)
- [Move-in-2D: 2D-Conditioned Human Motion Generation](https://mlsanity.com/paper/2412.13185/) (2024)
- [3D Human Interaction Generation: A Survey](https://mlsanity.com/paper/2503.13120/) (2025)
- [HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2409.04398/) (2024)
- [Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation](https://mlsanity.com/paper/2412.13454/) (2024)
- [Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots](https://mlsanity.com/paper/2603.06181/) (2026)
- [A Unified Framework for Human-centric Point Cloud Video Understanding](https://mlsanity.com/paper/2403.20031/) (2024)
- [ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate](https://mlsanity.com/paper/2503.21268/) (2025)
- [Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis](https://mlsanity.com/paper/2410.09312/) (2024)
- [SwimXYZ: A large-scale dataset of synthetic swimming motions and videos](https://mlsanity.com/paper/2310.04360/) (2023)
- [The Way Up: A Dataset for Hold Usage Detection in Sport Climbing](https://mlsanity.com/paper/2505.12854/) (2025)
- [FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision](https://mlsanity.com/paper/2603.19770/) (2026)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)
- [Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset](https://mlsanity.com/paper/2512.10321/) (2025)
- [B'ezier Degradation Modeling for LiDAR-based Human Motion Capture](https://mlsanity.com/paper/2605.19620/) (2026)
- [MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations](https://mlsanity.com/paper/2606.23000/) (2026)
- [Crowd4D: Scene-Aware Monocular 4D Crowd Reconstruction](https://mlsanity.com/paper/2607.19517/) (2026)
- [Training-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models](https://mlsanity.com/paper/2609.30026/) (2026)

## Abstract (arXiv)

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock ClImbing MotIon dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://www.lidarhumanmotion.net/cimi4d/.