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Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
Xinghao Chen, Guijin Wang, Hengkai Guo, Cairong Zhang
TL;DR
Single-depth-image 3D hand pose estimation remains difficult because of occlusion, articulation, noise, viewpoint variation, and finger self-similarity. Pose-REN uses estimated poses to guide feature extraction and hierarchically fuses joint features in an iterative cascade. Experiments on public datasets report performance surpassing state-of-the-art methods, with lower performance than REN for errors above roughly 30mm attributed to worse initial poses.
Problem
Single-depth-image 3D hand pose estimation remains challenging because of self-occlusion, complex articulation, depth-image noise and holes, viewpoint variation, and finger self-similarity.
Method
Pose-REN guides CNN feature-region extraction with a previously estimated pose, hierarchically fuses regions according to hand-joint topology, and iteratively refines pose predictions.
Results
Experiments on public hand pose datasets demonstrate that Pose-REN outperforms all state-of-the-art methods.
Takeaways & Limitations
Pose-REN provides a cascaded hand-pose estimation framework that combines pose-guided features with structured modeling of joint relations.
Takeaways & Limitations
For errors above roughly 30mm, Pose-REN’s fraction of good frames decreases slightly compared with REN, mainly because challenging samples have worse initial poses.
Abstract
from arXiv · showhide
Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.
1. Introduction
Single-depth-image 3D hand pose estimation matters for human-computer interaction but remains difficult because of occlusion, articulation, noise, viewpoint variation, and finger self-similarity. Pose-REN addresses limited feature extraction by guiding region selection with estimated poses, hierarchically fusing joint features, and iteratively refining predictions.
- Single-depth-image hand pose estimation is challenging because of self-occlusion, complex articulation, depth-image noise and holes, viewpoint variation, and finger self-similarity.
- CNN-based methods either infer 3D joint coordinates from heatmaps or directly regress coordinates, but prior work has paid limited attention to extracting optimal and representative CNN features.
- Pose-REN extracts feature regions under guidance from a previously estimated pose and hierarchically fuses joint features according to hand topology.
- The cascaded framework repeatedly refines the hand pose, using each stage’s refined estimate to guide feature-region extraction in the next stage.
- Pose-REN achieves the best performance against state-of-the-art methods on three public hand pose benchmarks, with ablations analyzing component contributions and robustness.
2. Related Work
Prior hand-pose methods include discriminative, generative, and hybrid approaches, with cascaded and hierarchical designs addressing refinement and joint structure. Pose-REN is a discriminative, model-free framework that uses pose-guided feature extraction and structured joint fusion to improve robustness and performance.
- Hand pose estimation methods are categorized as discriminative, generative, or hybrid, with discriminative approaches learning predictors directly from labelled data.
- Generative methods can handle self-occlusion and missing areas but require complex, time-consuming optimization and may become trapped in local optima.
- Hybrid methods combine discriminative and generative techniques but may require predefined hand-model properties, while synthetic-depth refinement can be sensitive to annotation errors.
- Pose-REN is a discriminative method that directly predicts 3D joint locations in a cascaded framework without a predefined hand model or postprocessing.
- Unlike REN’s grid extraction and simple fusion, Pose-REN uses estimated poses for region guidance and structured connections that model relations and constraints among hand joints.
- Pose-REN updates feature maps at each cascaded stage through an end-to-end framework rather than keeping initial features unchanged except for spatial transformation.
- Pose-REN hierarchically fuses features according to hand topology, integrating same-finger joints before combining features across fingers.
3. Pose Guided Structured Region Ensemble Network
Pose-REN refines hand pose estimates iteratively by extracting feature regions guided by a previous pose and fusing them according to hand-joint topology. It directly regresses the refined 3D pose without postprocessing.
- Framework overview: Pose-REN takes a depth image and a previously estimated pose to refine the 3D hand pose through cascaded regression stages.After T stages, the final estimate is P^T; the same model R is used at every inference refinement stage.
- Pose guided region extraction: A CNN generates feature maps, from which joint-specific rectangular regions are extracted using projected coordinates from the previous pose.The previous 3D joint coordinates are projected into image pixels using camera intrinsics and converted into feature-map coordinates for cropping.
- Pose guided region extraction: The extracted regions provide pose-guided features intended to be more optimal and representative for hand pose estimation.Figure 2 illustrates joint locations from the previous pose together with their corresponding cropped feature windows.
- Structured region ensemble: Feature regions are first fused among joints belonging to the same finger, then fused across fingers to regress the final hand pose.This hierarchical organization is designed to model constraints and correlations among hand joints more effectively than independent region connections.
- Structured region ensemble: The structured ensemble uses M regions and tree-structured fully connected layers to incorporate hand topology into feature fusion.Each region is processed by a fully connected layer before hierarchical concatenation; the final output is a 3 × J vector of 3D joint locations.
- Training strategy: Training iteratively builds augmented datasets by using each stage’s refined poses to train the next regression model.The final trained model is then used during inference to refine the initial hand pose iteratively.
4. Experiments
The experiments evaluate Pose-REN on three challenging public hand pose datasets and include ablation studies of its components and robustness.
- Evaluation design: Pose-REN is evaluated on the ICVL, NYU, and MSRA public hand pose benchmarks.The evaluation section introduces the datasets and metrics before testing the method and conducting ablation studies.
- Evaluation design: Ablation experiments examine the effectiveness and robustness of different components of the proposed framework.The supplied experiment description does not provide numerical ablation results.
4.1. Datasets
The study uses three public depth-based hand pose datasets with varied subjects, viewpoints, joint annotations, and evaluation protocols.
- NYU Hand Pose Dataset: NYU contains 72,757 training frames from one subject and 8,252 testing frames from two subjects, evaluated on 14 joints from the frontal view.One testing subject does not appear in training, and the dataset provides 36 annotated joints overall.
- MSRA Hand Pose Dataset: MSRA contains 76,500 frames from nine subjects and uses leave-one-subject-out cross-validation with 21 annotated joints.Its large viewpoint variation makes it a challenging dataset.
4.2. Evaluation Metric
The evaluation uses per-joint errors and success rate to measure hand pose accuracy across joints and test frames. Figures and tables apply these metrics to benchmark comparisons.
- Per-joint errors measure the average Euclidean distance between predicted and ground-truth locations for each joint across test frames.
- Success rate is the fraction of frames whose maximum joint error remains within a distance threshold τ.The indicator function equals one when the condition is true and zero otherwise.
- Figure 5 compares the proportion of good frames over error thresholds and per-joint errors on NYU.
- Figure 6 uses the same two views—good-frame proportions over thresholds and per-joint errors—for ICVL.
- Table 1 reports 2D average pixel errors and 3D average joint errors in millimeters on NYU.
4.3. Comparison with State-of-the-Arts
Pose-REN is compared with multiple state-of-the-art methods across NYU, ICVL, and MSRA using success rates and joint-error measures. It achieves the best reported performance overall, with robustness across viewpoints but a limitation at larger error thresholds.
- Compared methods: The evaluation compares Pose-REN with latent random forest, CNN, cascaded, feedback, multi-view, local-normal, occlusion-aware, and other state-of-the-art methods.
- Evaluation conditions: Some baseline results use online predicted labels, while others are estimated from figures and tables in the original papers.
- Evaluation conditions: For Mask R-CNN, depth images are cropped and resized to 96 × 96, with ResNet-50-FPN used as the backbone for comparison.
- NYU: On NYU, Pose-REN outperforms all compared methods, decreases 3D error by 0.88mm versus REN, and reduces 2D error by 2.7 pixels versus Mask R-CNN.
- ICVL: On ICVL, Pose-REN outperforms other methods with a large margin and reduces mean error versus REN by 0.514mm, a 7.04% relative improvement.
- MSRA: On MSRA, Pose-REN achieves the best performance among evaluated methods and has the smallest errors at almost all yaw and pitch angles.LSN is slightly better at relatively small yaw or pitch angles, but its performance decreases rapidly as viewpoint becomes larger.
- Limitation: For errors larger than around 30mm, Pose-REN’s fraction of good frames decreases slightly compared with REN because of worse initial poses on challenging samples.
4.4. Ablation Study
The ablation studies examine iteration count, pose-guided region extraction, structured region ensemble, and initialization robustness. Results support three iterations, pose-guided extraction, hierarchical structure, and robustness to initialization choices.
- Effect of iteration count: After one iteration, average joint error drops rapidly; it stabilizes as iterations increase, leading to the choice T = 3.
- Structured region ensemble: Table 2 compares average joint errors with and without structured region ensemble across three datasets, with bracketed percentages denoting error reduction.
- Pose-guided region extraction: Pose-guided region extraction outperforms grid-based REN under both matched region settings on NYU.
- Structured region ensemble: The structured-region comparison replaces hierarchical fully connected layers with two simple fully connected layers while keeping parameter counts similar.
- Initialization robustness: Pose-REN considerably improves several initialization methods during inference, supporting robustness over different initializations.
- Initialization robustness: With meanpose initialization, performance converges after 10 stages at an average joint error of 17.708mm.
- Initialization robustness: Training with a very poor initialization can still produce satisfying refined results, while a model trained on one initialization generalizes to others.
4.5. Qualitative Results
Qualitative comparisons show that Pose-REN progressively improves initialized hand poses and outperforms REN on challenging samples across three datasets.
- Iterative Refinement: Pose-REN gradually improves the estimated hand pose from initialization through stages 1–3, producing accurate results after several iterations.The figure compares the initialized pose, three refinement stages, and ground-truth annotation.
- Comparison with REN: Across three datasets, Pose-REN performs better than REN, including on some challenging samples.The qualitative comparison places REN-9x6x6 in the first row, Pose-REN in the second, and ground truth in the third.
5. Conclusion
Pose-REN extracts pose-guided CNN feature regions, integrates them according to hand-joint topology, and iteratively refines 3D hand pose estimates. Experiments demonstrate that it outperforms state-of-the-art methods, while the reported initialization analyses examine performance across error thresholds and per-joint errors.
- Method: Pose-REN estimates 3D hand pose from a single depth image using regions extracted from CNN feature maps under initial-pose guidance.The method targets more optimal and representative features for hand pose estimation.
- Method: The extracted regions are integrated hierarchically with tree-like connections that model hand-joint topology.This structured integration is part of the proposed region ensemble design.
- Inference: Pose-REN iteratively refines hand pose to obtain final estimated results.The conclusion describes iterative refinement as part of the final estimation procedure.
- Results: Experiments on public hand pose datasets demonstrate that Pose-REN outperforms all state-of-the-art methods.The paper also reports analyses using different initial poses and mean-pose initialization, evaluated by error thresholds and per-joint errors.