# Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture

arXiv: https://arxiv.org/abs/1808.01525 · HTML: https://mlsanity.com/paper/1808.01525/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2018.

## Built on by (most cited first, 17 of 17)

- [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.14304/) (2021)
- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [Generalizable Human Pose Triangulation](https://mlsanity.com/paper/2110.00280/) (2021)
- [Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors](https://mlsanity.com/paper/2106.14729/) (2021)
- [Learnable human mesh triangulation for 3D human pose and shape estimation](https://mlsanity.com/paper/2208.11251/) (2022)
- [Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network](https://mlsanity.com/paper/2105.10882/) (2021)
- [Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking](https://mlsanity.com/paper/2106.11589/) (2021)
- [FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction](https://mlsanity.com/paper/2105.01937/) (2021)
- [Progressive Multi-view Human Mesh Recovery with Self-Supervision](https://mlsanity.com/paper/2212.05223/) (2022)
- [HumMUSS: Human Motion Understanding using State Space Models](https://mlsanity.com/paper/2404.10880/) (2024)
- [Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training](https://mlsanity.com/paper/2112.13709/) (2021)
- [Multi-view Human Body Mesh Translator](https://mlsanity.com/paper/2210.01886/) (2022)
- [High Fidelity 3D Reconstructions with Limited Physical Views](https://mlsanity.com/paper/2110.11599/) (2021)
- [Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering](https://mlsanity.com/paper/2210.04514/) (2022)
- [Unconstrained Multi-view Human Pose Estimation with Algebraic Priors](https://mlsanity.com/paper/2604.24312/) (2026)
- [PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation](https://mlsanity.com/paper/2107.03000/) (2021)
- [SkelSplat: Robust Multi-view 3D Human Pose Estimation with Differentiable Gaussian Rendering](https://mlsanity.com/paper/2511.08294/) (2025)

## Abstract (arXiv)

We propose a CNN-based approach for multi-camera markerless motion capture of the human body. Unlike existing methods that first perform pose estimation on individual cameras and generate 3D models as post-processing, our approach makes use of 3D reasoning throughout a multi-stage approach. This novelty allows us to use provisional 3D models of human pose to rethink where the joints should be located in the image and to recover from past mistakes. Our principled refinement of 3D human poses lets us make use of image cues, even from images where we previously misdetected joints, to refine our estimates as part of an end-to-end approach. Finally, we demonstrate how the high-quality output of our multi-camera setup can be used as an additional training source to improve the accuracy of existing single camera models.