# Beyond Weak Perspective for Monocular 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/2009.06549 · HTML: https://mlsanity.com/paper/2009.06549/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021)
- [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022)
- [CameraHMR: Aligning People with Perspective](https://mlsanity.com/paper/2411.08128/) (2024)
- [Playing for 3D Human Recovery](https://mlsanity.com/paper/2110.07588/) (2021)
- [Occluded Human Mesh Recovery](https://mlsanity.com/paper/2203.13349/) (2022)
- [ReFit: Recurrent Fitting Network for 3D Human Recovery](https://mlsanity.com/paper/2308.11184/) (2023)
- [Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction](https://mlsanity.com/paper/2303.13796/) (2023)
- [WHAC: World-grounded Humans and Cameras](https://mlsanity.com/paper/2403.12959/) (2024)
- [PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation](https://mlsanity.com/paper/2211.11734/) (2022)
- [MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision](https://mlsanity.com/paper/2108.04869/) (2021)
- [JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery](https://mlsanity.com/paper/2307.16377/) (2023)
- [FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction](https://mlsanity.com/paper/2105.01937/) (2021)
- [SmartMocap: Joint Estimation of Human and Camera Motion using Uncalibrated RGB Cameras](https://mlsanity.com/paper/2209.13906/) (2022)
- [KITRO: Refining Human Mesh by 2D Clues and Kinematic-tree Rotation](https://mlsanity.com/paper/2405.19833/) (2024)
- [ReBaR: Reference-Based Reasoning for Robust Pose Estimation from Monocular Images](https://mlsanity.com/paper/2303.11675/) (2023)
- [Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery](https://mlsanity.com/paper/2411.16289/) (2024)
- [MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images](https://mlsanity.com/paper/2506.09919/) (2025)
- [W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised Calibration](https://mlsanity.com/paper/2311.17460/) (2023)
- [PersPose: 3D Human Pose Estimation with Perspective Encoding and Perspective Rotation](https://mlsanity.com/paper/2508.17239/) (2025)
- [Multimodal Active Measurement for Human Mesh Recovery in Close Proximity](https://mlsanity.com/paper/2310.08116/) (2023)
- [Personalized 3D Human Pose and Shape Refinement](https://mlsanity.com/paper/2403.11634/) (2024)
- [EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans](https://mlsanity.com/paper/2406.19726/) (2024)
- [CLHOP: Combined Audio-Video Learning for Horse 3D Pose and Shape Estimation](https://mlsanity.com/paper/2407.01244/) (2024)
- [PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation](https://mlsanity.com/paper/2606.26741/) (2026)
- [Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive Losses](https://mlsanity.com/paper/2402.02074/) (2024)
- [From Camera to World: A Plug-and-Play Module for Human Mesh Transformation](https://mlsanity.com/paper/2512.15212/) (2025)
- [Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture](https://mlsanity.com/paper/2603.09681/) (2026)

## Abstract (arXiv)

We consider the task of 3D joints location and orientation prediction from a monocular video with the skinned multi-person linear (SMPL) model. We first infer 2D joints locations with an off-the-shelf pose estimation algorithm. We use the SPIN algorithm and estimate initial predictions of body pose, shape and camera parameters from a deep regression neural network. We then adhere to the SMPLify algorithm which receives those initial parameters, and optimizes them so that inferred 3D joints from the SMPL model would fit the 2D joints locations. This algorithm involves a projection step of 3D joints to the 2D image plane. The conventional approach is to follow weak perspective assumptions which use ad-hoc focal length. Through experimentation on the 3D Poses in the Wild (3DPW) dataset, we show that using full perspective projection, with the correct camera center and an approximated focal length, provides favorable results. Our algorithm has resulted in a winning entry for the 3DPW Challenge, reaching first place in joints orientation accuracy.