# AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild

arXiv: https://arxiv.org/abs/2010.13302 · HTML: https://mlsanity.com/paper/2010.13302/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 30 of 40)

- [MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network](https://mlsanity.com/paper/2310.16288/) (2023)
- [GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video](https://mlsanity.com/paper/2307.05853/) (2023)
- [Human Pose as Compositional Tokens](https://mlsanity.com/paper/2303.11638/) (2023)
- [Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving](https://mlsanity.com/paper/2112.12141/) (2021)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.09554/) (2021)
- [PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation](https://mlsanity.com/paper/2209.08194/) (2022)
- [Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views](https://mlsanity.com/paper/2309.11962/) (2023)
- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.05092/) (2021)
- [ExpertAF: Expert Actionable Feedback from Video](https://mlsanity.com/paper/2408.00672/) (2024)
- [Deep learning for 3D human pose estimation and mesh recovery: A survey](https://mlsanity.com/paper/2402.18844/) (2024)
- [VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data](https://mlsanity.com/paper/2207.09949/) (2022)
- [HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning](https://mlsanity.com/paper/2302.00988/) (2023)
- [3D Human Pose Estimation Using M"obius Graph Convolutional Networks](https://mlsanity.com/paper/2203.10554/) (2022)
- [HumMUSS: Human Motion Understanding using State Space Models](https://mlsanity.com/paper/2404.10880/) (2024)
- [UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues](https://mlsanity.com/paper/2404.14634/) (2024)
- [Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation](https://mlsanity.com/paper/2408.15810/) (2024)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras](https://mlsanity.com/paper/2510.24464/) (2025)
- [VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation](https://mlsanity.com/paper/2410.18723/) (2024)
- [Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion](https://mlsanity.com/paper/2503.11088/) (2025)
- [Geometry-Biased Transformer for Robust Multi-View 3D Human Pose Reconstruction](https://mlsanity.com/paper/2312.17106/) (2023)
- [SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers](https://mlsanity.com/paper/2404.12625/) (2024)
- [HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery](https://mlsanity.com/paper/2412.04456/) (2024)
- [An uncertainty-aware framework for data-efficient multi-view animal pose estimation](https://mlsanity.com/paper/2510.09903/) (2025)
- [Semi-supervised Dense Keypoints Using Unlabeled Multiview Images](https://mlsanity.com/paper/2109.09299/) (2021)
- [Unconstrained Multi-view Human Pose Estimation with Algebraic Priors](https://mlsanity.com/paper/2604.24312/) (2026)
- [RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling](https://mlsanity.com/paper/2311.14242/) (2023)
- [Self-learning Canonical Space for Multi-view 3D Human Pose Estimation](https://mlsanity.com/paper/2403.12440/) (2024)

## Abstract (arXiv)

Occlusion is probably the biggest challenge for human pose estimation in the wild. Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints. To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views. The core of AdaFuse is to determine the point-point correspondence between two views which we solve effectively by exploring the sparsity of the heatmap representation. We also learn an adaptive fusion weight for each camera view to reflect its feature quality in order to reduce the chance that good features are undesirably corrupted by ``bad'' views. The fusion model is trained end-to-end with the pose estimation network, and can be directly applied to new camera configurations without additional adaptation. We extensively evaluate the approach on three public datasets including Human3.6M, Total Capture and CMU Panoptic. It outperforms the state-of-the-arts on all of them. We also create a large scale synthetic dataset Occlusion-Person, which allows us to perform numerical evaluation on the occluded joints, as it provides occlusion labels for every joint in the images. The dataset and code are released at https://github.com/zhezh/adafuse-3d-human-pose.