# On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation arXiv: https://arxiv.org/abs/2203.15865 ยท HTML: https://mlsanity.com/paper/2203.15865/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 45 of its 65 references that are in the map. - First posted on arXiv 29 Mar 2022. ## Builds on (most cited first, 30 of 45) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017) - [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016) - [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017) - [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017) - [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019) - [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016) - [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) - [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) - [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016) - [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019) - [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017) - [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017) - [Hand Pose Estimation via Latent 2.5D Heatmap Regression](https://mlsanity.com/paper/1804.09534/) (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) - [Unsupervised 3D Pose Estimation with Geometric Self-Supervision](https://mlsanity.com/paper/1904.04812/) (2019) - [SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach](https://mlsanity.com/paper/2007.09389/) (2020) - [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021) - [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018) - [RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1902.09868/) (2019) - [Cascaded deep monocular 3D human pose estimation with evolutionary training data](https://mlsanity.com/paper/2006.07778/) (2020) - [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017) - [Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation](https://mlsanity.com/paper/1804.01110/) (2018) - [Learning Monocular 3D Human Pose Estimation from Multi-view Images](https://mlsanity.com/paper/1803.04775/) (2018) ## Built on by (most cited first, 10 of 10) - [MEGA: Masked Generative Autoencoder for Human Mesh Recovery](https://mlsanity.com/paper/2405.18839/) (2024) - [Improved Trajectory Reconstruction for Markerless Pose Estimation](https://mlsanity.com/paper/2303.02413/) (2023) - [Markerless Motion Capture and Biomechanical Analysis Pipeline](https://mlsanity.com/paper/2303.10654/) (2023) - [Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants](https://mlsanity.com/paper/2605.17120/) (2026) - [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023) - [Two Views Are Better than One: Monocular 3D Pose Estimation with Multiview Consistency](https://mlsanity.com/paper/2311.12421/) (2023) - [Occlusion Resilient 3D Human Pose Estimation](https://mlsanity.com/paper/2402.11036/) (2024) - [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026) - [Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements](https://mlsanity.com/paper/2607.02796/) (2026) - [Cross-Model Distillation of a Human-Pose Foundation Model from Unannotated Infant Video for Markerless 3D Pose Estimation](https://mlsanity.com/paper/2609.01840/) (2026) ## Abstract (arXiv) Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent attention has shifted towards semi- and weakly-supervised learning. Generating an effective form of supervision with little annotations still poses major challenge in crowded scenes. In this paper we propose to impose multi-view geometrical constraints by means of a weighted differentiable triangulation and use it as a form of self-supervision when no labels are available. We therefore train a 2D pose estimator in such a way that its predictions correspond to the re-projection of the triangulated 3D pose and train an auxiliary network on them to produce the final 3D poses. We complement the triangulation with a weighting mechanism that alleviates the impact of noisy predictions caused by self-occlusion or occlusion from other subjects. We demonstrate the effectiveness of our semi-supervised approach on Human3.6M and MPI-INF-3DHP datasets, as well as on a new multi-view multi-person dataset that features occlusion.