# Learning Temporal 3D Human Pose Estimation with Pseudo-Labels

arXiv: https://arxiv.org/abs/2110.07578 · HTML: https://mlsanity.com/paper/2110.07578/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [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)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Self-Supervised Learning of 3D Human Pose using Multi-view Geometry](https://mlsanity.com/paper/1903.02330/) (2019)
- [Unsupervised 3D Pose Estimation with Geometric Self-Supervision](https://mlsanity.com/paper/1904.04812/) (2019)
- [RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1902.09868/) (2019)
- [Learning Monocular 3D Human Pose Estimation from Multi-view Images](https://mlsanity.com/paper/1803.04775/) (2018)
- [Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild](https://mlsanity.com/paper/2003.07581/) (2020)
- [CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild](https://mlsanity.com/paper/2011.14679/) (2020)
- [In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations](https://mlsanity.com/paper/1904.03289/) (2019)
- [Can 3D Pose be Learned from 2D Projections Alone?](https://mlsanity.com/paper/1808.07182/) (2018)
- [Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis](https://mlsanity.com/paper/2004.04400/) (2020)
- [Deep Kinematic Pose Regression](https://mlsanity.com/paper/1609.05317/) (2016)
- [OriNet: A Fully Convolutional Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1811.04989/) (2018)
- [Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision](https://mlsanity.com/paper/1705.11166/) (2017)
- [Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses](https://mlsanity.com/paper/2008.05770/) (2020)
- [Traffic Control Gesture Recognition for Autonomous Vehicles](https://mlsanity.com/paper/2007.16072/) (2020)
- [Single Image 3D Interpreter Network](https://mlsanity.com/paper/1604.08685/) (2016)
- [Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach](https://mlsanity.com/paper/1509.04309/) (2015)
- [Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets](https://mlsanity.com/paper/1901.02000/) (2019)
- [Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry](https://mlsanity.com/paper/2108.07777/) (2021)
- [PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation](https://mlsanity.com/paper/2003.03473/) (2020)

## Built on by (most cited first, 4 of 4)

- [Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2307.14889/) (2023)
- [A Spatio-Temporal Multilayer Perceptron for Gesture Recognition](https://mlsanity.com/paper/2204.11511/) (2022)
- [A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization](https://mlsanity.com/paper/2310.03205/) (2023)
- [PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimation](https://mlsanity.com/paper/2510.19475/) (2025)

## Abstract (arXiv)

We present a simple, yet effective, approach for self-supervised 3D human pose estimation. Unlike the prior work, we explore the temporal information next to the multi-view self-supervision. During training, we rely on triangulating 2D body pose estimates of a multiple-view camera system. A temporal convolutional neural network is trained with the generated 3D ground-truth and the geometric multi-view consistency loss, imposing geometrical constraints on the predicted 3D body skeleton. During inference, our model receives a sequence of 2D body pose estimates from a single-view to predict the 3D body pose for each of them. An extensive evaluation shows that our method achieves state-of-the-art performance in the Human3.6M and MPI-INF-3DHP benchmarks. Our code and models are publicly available at urlhttps://github.com/vru2020/TMHPE/.