# 3D Human Pose Estimation in RGBD Images for Robotic Task Learning

arXiv: https://arxiv.org/abs/1803.02622 · HTML: https://mlsanity.com/paper/1803.02622/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

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

- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving](https://mlsanity.com/paper/2112.12141/) (2021)
- [DITTO: Demonstration Imitation by Trajectory Transformation](https://mlsanity.com/paper/2403.15203/) (2024)
- [DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.02796/) (2022)
- [Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR](https://mlsanity.com/paper/2211.16951/) (2022)
- [HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2212.07729/) (2022)
- [Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2311.12028/) (2023)
- [Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation](https://mlsanity.com/paper/2501.05264/) (2025)
- [LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network](https://mlsanity.com/paper/2306.12525/) (2023)
- [3D Human Pose Regression using Graph Convolutional Network](https://mlsanity.com/paper/2105.10379/) (2021)
- [Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment](https://mlsanity.com/paper/2301.06648/) (2023)
- [Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study](https://mlsanity.com/paper/2108.01495/) (2021)
- [Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination](https://mlsanity.com/paper/2205.07209/) (2022)
- [HANDI: Hand-Centric Text-and-Image Conditioned Video Generation](https://mlsanity.com/paper/2412.04189/) (2024)
- [mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction](https://mlsanity.com/paper/2503.02375/) (2025)
- [H2OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers](https://mlsanity.com/paper/2509.06956/) (2025)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [Combined Task and Action Learning from Human Demonstrations for Mobile Manipulation Applications](https://mlsanity.com/paper/1908.10184/) (2019)
- [Heuristic Weakly Supervised 3D Human Pose Estimation](https://mlsanity.com/paper/2105.10996/) (2021)
- [GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding](https://mlsanity.com/paper/2505.10671/) (2025)
- [Human keypoint detection for close proximity human-robot interaction](https://mlsanity.com/paper/2207.07742/) (2022)
- [Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB](https://mlsanity.com/paper/2409.11104/) (2024)
- [Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms](https://mlsanity.com/paper/2504.12699/) (2025)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization](https://mlsanity.com/paper/2106.15409/) (2021)
- [A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements](https://mlsanity.com/paper/2107.11214/) (2021)
- [DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors](https://mlsanity.com/paper/2110.07283/) (2021)
- [From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation](https://mlsanity.com/paper/2507.12884/) (2025)
- [Efficient Diffusion-Based 3D Human Pose Estimation with Hierarchical Temporal Pruning](https://mlsanity.com/paper/2508.21363/) (2025)
- [A3RNN: Bi-directional Fusion of Bottom-up and Top-down Process for Developmental Visual Attention in Robots](https://mlsanity.com/paper/2510.10221/) (2025)

## Abstract (arXiv)

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.