# Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery

arXiv: https://arxiv.org/abs/2206.10457 · HTML: https://mlsanity.com/paper/2206.10457/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 45 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [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)
- [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)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [Learning Category-Specific Mesh Reconstruction from Image Collections](https://mlsanity.com/paper/1803.07549/) (2018)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [Unite the People: Closing the Loop Between 3D and 2D Human Representations](https://mlsanity.com/paper/1701.02468/) (2017)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Populating 3D Scenes by Learning Human-Scene Interaction](https://mlsanity.com/paper/2012.11581/) (2020)
- [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021)
- [Generating 3D People in Scenes without People](https://mlsanity.com/paper/1912.02923/) (2019)
- [Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation](https://mlsanity.com/paper/2004.03686/) (2020)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training](https://mlsanity.com/paper/2004.11822/) (2020)
- [Holistic 3D Human and Scene Mesh Estimation from Single View Images](https://mlsanity.com/paper/2012.01591/) (2020)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data](https://mlsanity.com/paper/2011.00980/) (2020)
- [In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations](https://mlsanity.com/paper/1904.03289/) (2019)
- [Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction](https://mlsanity.com/paper/2103.16449/) (2021)
- [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](https://mlsanity.com/paper/2004.01166/) (2020)

## Built on by (most cited first, 12 of 12)

- [Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM](https://mlsanity.com/paper/2401.12175/) (2024)
- [ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image](https://mlsanity.com/paper/2305.16411/) (2023)
- [Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction](https://mlsanity.com/paper/2308.06554/) (2023)
- [KBody: Towards general, robust, and aligned monocular whole-body estimation](https://mlsanity.com/paper/2304.11542/) (2023)
- [Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains](https://mlsanity.com/paper/2303.09541/) (2023)
- [NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action](https://mlsanity.com/paper/2212.13660/) (2022)
- [ESCAPE: Energy-based Selective Adaptive Correction for Out-of-distribution 3D Human Pose Estimation](https://mlsanity.com/paper/2407.14605/) (2024)
- [Ask, Pose, Unite: Scaling Data Acquisition for Close Interactions with Vision Language Models](https://mlsanity.com/paper/2410.00309/) (2024)
- [Semantics-aware Test-time Adaptation for 3D Human Pose Estimation](https://mlsanity.com/paper/2502.10724/) (2025)
- [Online Test-time Adaptation for 3D Human Pose Estimation: A Practical Perspective with Estimated 2D Poses](https://mlsanity.com/paper/2503.11194/) (2025)
- [Robust Long-term Test-Time Adaptation for 3D Human Pose Estimation through Motion Discretization](https://mlsanity.com/paper/2511.18851/) (2025)
- [UnrealPose: Leveraging Game Engine Kinematics for Large-Scale Synthetic Human Pose Data](https://mlsanity.com/paper/2601.00991/) (2026)

## Abstract (arXiv)

The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare. A fundamental challenge in human mesh recovery is in collecting the ground truth 3D mesh targets required for training, which requires burdensome motion capturing systems and is often limited to indoor laboratories. As a result, while progress is made on benchmark datasets collected in these restrictive settings, models fail to generalize to real-world "in-the-wild" scenarios due to distribution shifts. We propose Domain Adaptive 3D Pose Augmentation (DAPA), a data augmentation method that enhances the model's generalization ability in in-the-wild scenarios. DAPA combines the strength of methods based on synthetic datasets by getting direct supervision from the synthesized meshes, and domain adaptation methods by using ground truth 2D keypoints from the target dataset. We show quantitatively that finetuning with DAPA effectively improves results on benchmarks 3DPW and AGORA. We further demonstrate the utility of DAPA on a challenging dataset curated from videos of real-world parent-child interaction.