# FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction

arXiv: https://arxiv.org/abs/2105.01937 · HTML: https://mlsanity.com/paper/2105.01937/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 87 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 30 of 55)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [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)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video](https://mlsanity.com/paper/2011.08627/) (2020)
- [DeepCap: Monocular Human Performance Capture Using Weak Supervision](https://mlsanity.com/paper/2003.08325/) (2020)
- [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019)
- [Epipolar Transformers](https://mlsanity.com/paper/2005.04551/) (2020)
- [QuaterNet: A Quaternion-based Recurrent Model for Human Motion](https://mlsanity.com/paper/1805.06485/) (2018)
- [Ordinal Depth Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/1805.04095/) (2018)
- [Self-Supervised Learning of 3D Human Pose using Multi-view Geometry](https://mlsanity.com/paper/1903.02330/) (2019)
- [3D Human Motion Estimation via Motion Compression and Refinement](https://mlsanity.com/paper/2008.03789/) (2020)
- [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.14304/) (2021)
- [Cross View Fusion for 3D Human Pose Estimation](https://mlsanity.com/paper/1909.01203/) (2019)
- [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [Motion Guided 3D Pose Estimation from Videos](https://mlsanity.com/paper/2004.13985/) (2020)

## Built on by (most cited first, 11 of 11)

- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [MoDi: Unconditional Motion Synthesis from Diverse Data](https://mlsanity.com/paper/2206.08010/) (2022)
- [Proactive Multi-Camera Collaboration For 3D Human Pose Estimation](https://mlsanity.com/paper/2303.03767/) (2023)
- [Markerless human pose estimation for biomedical applications: a survey](https://mlsanity.com/paper/2308.00519/) (2023)
- [RAM: Recover Any 3D Human Motion in-the-Wild](https://mlsanity.com/paper/2603.19929/) (2026)
- [Learning Multi-view Anomaly Detection with Efficient Adaptive Selection](https://mlsanity.com/paper/2407.11935/) (2024)
- [UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues](https://mlsanity.com/paper/2404.14634/) (2024)
- [MLPHand: Real Time Multi-View 3D Hand Mesh Reconstruction via MLP Modeling](https://mlsanity.com/paper/2406.16137/) (2024)
- [YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras](https://mlsanity.com/paper/2511.16521/) (2025)
- [Self-learning Canonical Space for Multi-view 3D Human Pose Estimation](https://mlsanity.com/paper/2403.12440/) (2024)
- [STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation](https://mlsanity.com/paper/2609.24482/) (2026)

## Abstract (arXiv)

The increasing availability of video recordings made by multiple cameras has offered new means for mitigating occlusion and depth ambiguities in pose and motion reconstruction methods. Yet, multi-view algorithms strongly depend on camera parameters; particularly, the relative transformations between the cameras. Such a dependency becomes a hurdle once shifting to dynamic capture in uncontrolled settings. We introduce FLEX (Free muLti-view rEconstruXion), an end-to-end extrinsic parameter-free multi-view model. FLEX is extrinsic parameter-free (dubbed ep-free) in the sense that it does not require extrinsic camera parameters. Our key idea is that the 3D angles between skeletal parts, as well as bone lengths, are invariant to the camera position. Hence, learning 3D rotations and bone lengths rather than locations allows predicting common values for all camera views. Our network takes multiple video streams, learns fused deep features through a novel multi-view fusion layer, and reconstructs a single consistent skeleton with temporally coherent joint rotations. We demonstrate quantitative and qualitative results on three public datasets, and on synthetic multi-person video streams captured by dynamic cameras. We compare our model to state-of-the-art methods that are not ep-free and show that in the absence of camera parameters, we outperform them by a large margin while obtaining comparable results when camera parameters are available. Code, trained models, and other materials are available on our project page.