# AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation

arXiv: https://arxiv.org/abs/2112.11593 · HTML: https://mlsanity.com/paper/2112.11593/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 51 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 30 of 40)

- [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)
- [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)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [Learning to Estimate 3D Human Pose and Shape from a Single Color Image](https://mlsanity.com/paper/1805.04092/) (2018)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017)
- [Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation](https://mlsanity.com/paper/2004.03686/) (2020)
- [Exploiting temporal context for 3D human pose estimation in the wild](https://mlsanity.com/paper/1905.04266/) (2019)
- [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)
- [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [3D Human Pose Estimation in the Wild by Adversarial Learning](https://mlsanity.com/paper/1803.09722/) (2018)
- [RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1902.09868/) (2019)
- [Motion Guided 3D Pose Estimation from Videos](https://mlsanity.com/paper/2004.13985/) (2020)
- [Cascaded deep monocular 3D human pose estimation with evolutionary training data](https://mlsanity.com/paper/2006.07778/) (2020)
- [Sim2real transfer learning for 3D human pose estimation: motion to the rescue](https://mlsanity.com/paper/1907.02499/) (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)
- [3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training](https://mlsanity.com/paper/2004.11822/) (2020)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [Body Meshes as Points](https://mlsanity.com/paper/2105.02467/) (2021)

## Built on by (most cited first, 30 of 32)

- [Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation](https://mlsanity.com/paper/2307.03833/) (2023)
- [Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation](https://mlsanity.com/paper/2303.16456/) (2023)
- [A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation](https://mlsanity.com/paper/2403.11310/) (2024)
- [PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.09678/) (2023)
- [Progressive Multi-view Human Mesh Recovery with Self-Supervision](https://mlsanity.com/paper/2212.05223/) (2022)
- [Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains](https://mlsanity.com/paper/2303.09541/) (2023)
- [MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling](https://mlsanity.com/paper/2306.17201/) (2023)
- [Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation](https://mlsanity.com/paper/2403.06164/) (2024)
- [Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment](https://mlsanity.com/paper/2408.08182/) (2024)
- [FMPose3D: monocular 3D pose estimation via flow matching](https://mlsanity.com/paper/2602.05755/) (2026)
- [Multimodal Active Measurement for Human Mesh Recovery in Close Proximity](https://mlsanity.com/paper/2310.08116/) (2023)
- [UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning](https://mlsanity.com/paper/2311.16477/) (2023)
- [PoseGen: Learning to Generate 3D Human Pose Dataset with NeRF](https://mlsanity.com/paper/2312.14915/) (2023)
- [Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation](https://mlsanity.com/paper/2402.02339/) (2024)
- [Recovering Complete Actions for Cross-dataset Skeleton Action Recognition](https://mlsanity.com/paper/2410.23641/) (2024)
- [PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation via Pose Lifting Networks](https://mlsanity.com/paper/2505.10888/) (2025)
- [RUMPL: Ray-Based Transformers for Universal Multi-View 2D to 3D Human Pose Lifting](https://mlsanity.com/paper/2512.15488/) (2025)
- [EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans](https://mlsanity.com/paper/2406.19726/) (2024)
- [ESCAPE: Energy-based Selective Adaptive Correction for Out-of-distribution 3D Human Pose Estimation](https://mlsanity.com/paper/2407.14605/) (2024)
- [PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Data](https://mlsanity.com/paper/2503.13025/) (2025)
- [Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms](https://mlsanity.com/paper/2504.12699/) (2025)
- [UniHPR: Unified Human Pose Representation via Singular Value Contrastive Learning](https://mlsanity.com/paper/2510.19078/) (2025)
- [Lifelong Domain Adaptive 3D Human Pose Estimation](https://mlsanity.com/paper/2512.23860/) (2025)
- [Mirror-Aware Neural Humans](https://mlsanity.com/paper/2309.04750/) (2023)
- [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)
- [SHARE: Single-view Human Adversarial REconstruction](https://mlsanity.com/paper/2401.00343/) (2023)
- [Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach](https://mlsanity.com/paper/2405.20084/) (2024)
- [Leveraging 2D Masked Reconstruction for Domain Adaptation of 3D Pose Estimation](https://mlsanity.com/paper/2501.08408/) (2025)
- [Toward Efficient Generalization in 3D Human Pose Estimation via a Canonical Domain Approach](https://mlsanity.com/paper/2501.16146/) (2025)

## Abstract (arXiv)

This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by improving the diversity of the training data. We argue that diversity alone is not sufficient and that the characteristics of the training data need to be adapted to those of the new dataset such as camera viewpoint, position, human actions, and body size. To this end, we propose AdaptPose, an end-to-end framework that generates synthetic 3D human motions from a source dataset and uses them to fine-tune a 3D pose estimator. AdaptPose follows an adversarial training scheme. From a source 3D pose the generator generates a sequence of 3D poses and a camera orientation that is used to project the generated poses to a novel view. Without any 3D labels or camera information AdaptPose successfully learns to create synthetic 3D poses from the target dataset while only being trained on 2D poses. In experiments on the Human3.6M, MPI-INF-3DHP, 3DPW, and Ski-Pose datasets our method outperforms previous work in cross-dataset evaluations by 14% and previous semi-supervised learning methods that use partial 3D annotations by 16%.