# PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose

arXiv: https://arxiv.org/abs/1910.12029 · HTML: https://mlsanity.com/paper/1910.12029/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization](https://mlsanity.com/paper/2203.11471/) (2022)
- [VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data](https://mlsanity.com/paper/2207.09949/) (2022)
- [Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimation](https://mlsanity.com/paper/2208.03704/) (2022)
- [HumMUSS: Human Motion Understanding using State Space Models](https://mlsanity.com/paper/2404.10880/) (2024)
- [PoseKernelLifter: Metric Lifting of 3D Human Pose using Sound](https://mlsanity.com/paper/2112.00216/) (2021)
- [MuPPet: Multi-person 2D-to-3D Pose Lifting](https://mlsanity.com/paper/2604.09715/) (2026)
- [Object Agnostic 3D Lifting in Space and Time](https://mlsanity.com/paper/2412.01166/) (2024)
- [Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions](https://mlsanity.com/paper/2605.22697/) (2026)
- [Fine-grained Human Motion Understanding with Language Models](https://mlsanity.com/paper/2606.20888/) (2026)

## Abstract (arXiv)

This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute 3D location of a target subject and generates an improved 3D relative pose estimation compared with existing pose-lifting methods. Using the PoseLifter with a 2D pose estimator in a cascade fashion can estimate a 3D human pose from a single RGB image. In this case, we empirically prove that using realistic 2D poses synthesized with the real error distribution of 2D body joints considerably improves the performance of our PoseLifter. The proposed method is applied to public datasets to achieve state-of-the-art 2D-to-3D pose lifting and 3D human pose estimation.