# Feature Boosting Network For 3D Pose Estimation

arXiv: https://arxiv.org/abs/1901.04877 · HTML: https://mlsanity.com/paper/1901.04877/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 26 of 26)

- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.14304/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Instance-Specific Feature Propagation for Referring Segmentation](https://mlsanity.com/paper/2204.12109/) (2022)
- [DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders](https://mlsanity.com/paper/2108.08557/) (2021)
- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2210.16829/) (2022)
- [Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction](https://mlsanity.com/paper/2311.17408/) (2023)
- [Deep learning for 3D human pose estimation and mesh recovery: A survey](https://mlsanity.com/paper/2402.18844/) (2024)
- [An Adversarial Human Pose Estimation Network Injected with Graph Structure](https://mlsanity.com/paper/2103.15534/) (2021)
- [HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation](https://mlsanity.com/paper/2503.23331/) (2025)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation](https://mlsanity.com/paper/2208.11960/) (2022)
- [Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head](https://mlsanity.com/paper/2309.17143/) (2023)
- [Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimation](https://mlsanity.com/paper/2312.15636/) (2023)
- [CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting](https://mlsanity.com/paper/2309.16140/) (2023)
- [Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives](https://mlsanity.com/paper/2205.12627/) (2022)
- [Double-chain Constraints for 3D Human Pose Estimation in Images and Videos](https://mlsanity.com/paper/2308.05298/) (2023)
- [Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation](https://mlsanity.com/paper/2111.06500/) (2021)
- [Spatial Feature Mapping for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2206.01831/) (2022)
- [Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning](https://mlsanity.com/paper/2508.10897/) (2025)
- [Real-time, low-cost multi-person 3D pose estimation](https://mlsanity.com/paper/2110.11414/) (2021)
- [Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage](https://mlsanity.com/paper/2202.12259/) (2022)
- [Capsules as viewpoint learners for human pose estimation](https://mlsanity.com/paper/2302.06194/) (2023)
- [Augmenting Vision-Based Human Pose Estimation with Rotation Matrix](https://mlsanity.com/paper/2310.06068/) (2023)
- [Face recognition on point cloud with cgan-top for denoising](https://mlsanity.com/paper/2506.06864/) (2025)

## Abstract (arXiv)

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.