Source-linked AI summary
Fast Human Pose Estimation
Feng Zhang, Xiatian Zhu, Mao Ye
TL;DR
Human pose estimation has emphasized generalization while leaving model efficiency and deployment cost under-studied. The paper introduces Fast Pose Distillation, which combines a lightweight pose network with teacher-to-student transfer of pose structure knowledge; evaluations on MPII Human Pose and Leeds Sports Pose report improved cost-effectiveness without accuracy compromise. Training tiny pose networks remains empirically non-trivial.
Problem
Existing pose-estimation approaches often use resource-intensive models, leaving efficiency and scalability under-studied despite their importance for deployment.
Method
FPD trains a lightweight pose CNN by transferring latent pose knowledge from a pre-trained larger teacher to a tiny target model.
Results
FPD achieves faster, more cost-effective inference with an extremely smaller model size while reaching the same level of human pose prediction performance as top-performing alternatives.
Takeaways & Limitations
The method supports highly efficient human pose models for scaling deployment while preserving accuracy performance.
Takeaways & Limitations
Tiny pose networks are empirically non-trivial to train.
Abstract
from arXiv · showhide
Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside the significant efficiency problem. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate the under-studied but practically critical pose model efficiency problem. To this end, we present a new Fast Pose Distillation (FPD) model learning strategy. Specifically, the FPD trains a lightweight pose neural network architecture capable of executing rapidly with low computational cost. It is achieved by effectively transferring the pose structure knowledge of a strong teacher network. Extensive evaluations demonstrate the advantages of our FPD method over a broad range of state-of-the-art pose estimation approaches in terms of model cost-effectiveness on two standard benchmark datasets, MPII Human Pose and Leeds Sports Pose.
1. Introduction
The paper targets the under-studied efficiency and deployment-cost problem in human pose estimation by combining a lightweight pose architecture with Fast Pose Distillation (FPD). FPD transfers pose knowledge from a strong teacher to a small student, aiming to retain comparable accuracy while reducing inference cost, and is evaluated on MPII Human Pose and Leeds Sports Pose.
- Motivation: Existing pose models improve performance using resource-intensive networks, causing inefficient inference and poor scalability on resource-limited devices.The cited passage attributes the cost to large network depth and width and reports per-image costs at tens of FLOPs.
- Motivation: The paper investigates pose-estimation efficiency while preserving comparable accuracy rather than pursuing accuracy alone.It frames efficiency as critical for scaling deep pose-estimation methods to real applications.
- Method: FPD transfers latent pose knowledge from a pre-trained larger teacher model to a tiny target pose model under limited computational budgets.The method uses a pose knowledge-distillation objective and targets less than 20% of the cost required by similarly strong alternatives.
- Architecture: The proposed lightweight Hourglass architecture reduces model cost by examining redundancy in existing state-of-the-art pose CNN designs while retaining learning capacity.The design focuses on channel and stage redundancy in Hourglass-style networks.
- Evaluation: Extensive comparisons evaluate FPD against a broad range of state-of-the-art approaches on MPII Human Pose and Leeds Sports Pose.The evaluation emphasizes the balance between model inference efficiency and prediction performance.
2. Related Work
Prior pose-estimation work largely prioritizes accuracy with computationally expensive models, limiting deployment under tight computing budgets. This paper studies efficiency while preserving performance and distinguishes FPD by transferring structured joint-confidence-map information rather than only category-level knowledge.
- Human Pose Estimation: Prior human pose-estimation methods mainly improve accuracy with complex, computationally expensive models, restricting scalability and deployability in real-world applications.The limitation is especially relevant when available computing budgets are very limited.
- Efficient Pose Estimation: Parameter binarisation improves execution speed for resource-limited platforms but causes a dramatic performance drop, while another efficiency method lacks quantitative trade-off evaluation.These shortcomings motivate evaluating efficiency alongside effectiveness more systematically.
- Efficient Pose Estimation: This work systematically studies pose-estimation efficiency under preserved model performance to improve usability and reliability in real-world scenarios.The stated comparison condition is efficiency without sacrificing the model performance rate.
- Knowledge Distillation: Knowledge distillation transfers information between neural networks with distinct capacities, commonly using teacher supervision such as class probabilities or feature representations.The related literature includes offline and online distillation and transfers across layers or training states.
- Knowledge Distillation: Unlike prior category-level distillation, FPD transfers richer structured information from dense joint confidence maps and targets model efficiency rather than occlusion handling.The cited comparison distinguishes FPD from a radio-signals-based pose method.
3. Fast Human Pose Estimation
Fast Human Pose Distillation addresses the efficiency gap in human pose estimation by combining a compact pose network with knowledge distillation from a stronger teacher. The method targets fast, cost-effective inference while preserving pose estimation performance and improving training of extremely small networks.
- Pose estimation formulation: Human pose estimation predicts spatial coordinates of human joints from images, commonly using joint confidence maps and MSE-based supervised training.Each joint is represented by a Gaussian confidence map, and the model is optimized by matching predicted and ground-truth maps.
- Efficiency problem: Existing pose methods often prioritize model performance over inference efficiency, producing resource-intensive networks with poor scalability on devices such as smartphones and robots.Large depth and width increase per-image FLOPs, while binarized networks can suffer weak generalization capacity.
- Fast Pose Distillation: The proposed Fast Pose Distillation strategy combines a lightweight Hourglass-style target architecture with a pretrained strong teacher model.The teacher supplies additional supervision during training, while only the compact target network is retained for deployment.
- Pose knowledge distillation: The pose-specific distillation objective matches teacher and student joint confidence maps using an MSE-based mimicry loss unsuitable for ordinary single-label classification distillation.The overall loss combines the distillation term with the ground-truth MSE loss using a cross-validated balancing weight α.
- Supervision enhancement: Teacher supervision can provide additional or softened guidance for erroneous, difficult, or incomplete annotations, but the method is tolerant to either annotation error rather than co-occurring errors.The paper identifies benefits for wrong joint labels, cluttered or occluded examples, and missing joint labels, while explicitly limiting tolerance when errors co-occur.
4. Experiments
Experiments on MPII and LSP evaluate FPD using standard pose-accuracy metrics alongside computational cost. Results show that lightweight architectures and teacher knowledge transfer preserve strong accuracy while substantially improving cost-effectiveness.
- Experiment Setup: The experiments use MPII and LSP, with PCKh@0.5 for MPII and PCK@0.2 for LSP, while FLOPs measure efficiency.MPII includes 16 labelled joints per person, whereas LSP includes 14.
- Comparisons to State-Of-The-Art Methods: 91.1% mean PCKh@0.5 is achieved by FPD on MPII without clearly compromising generalisation capability.Compared with the best performer, FPD uses 14.3% computational cost while retaining 96.4% of mean PCKh performance.
- Comparisons to State-Of-The-Art Methods: 2.9× greater efficiency and a 4.8% mean PCKh gain are obtained over the most efficient alternative competitor on MPII.The comparison is against model, using computational costs of 26 and 9, respectively.
- Comparisons to State-Of-The-Art Methods: FPD achieves the best pose prediction accuracy among compared LSP methods while running more efficiently and consuming much less training energy than the competitive alternative.The reported LSP metric is PCK@0.2 on the test dataset.
- Ablation Study: Reducing Hourglass depth and width yields a model with 16% computational cost and 98% model performance relative to the state-of-the-art design.The analysis removes half the stages and half the channels, indicating substantial redundancy in the original architecture.
- Ablation Study: Teacher knowledge transfer adds 0.8% mean PCKh accuracy, while FPD generalises to another backbone with a 1.0% mean PCKh@0.5 gain.Visualisations associate teacher mimicry with error labelling, difficult images, and missing annotations.
5. Conclusion
The Fast Pose Distillation (FPD) strategy combines a lightweight pose CNN architecture with pose-structure knowledge distillation from a large teacher model to improve human pose model cost-effectiveness. Extensive evaluations report superiority over state-of-the-art alternatives on two benchmark datasets without accuracy compromise.
- FPD develops a lightweight human pose CNN architecture and transfers pose structure knowledge from a large teacher model to a lightweight student.The strategy is designed to improve model cost-effectiveness while preserving prediction performance.
- FPD achieves highly efficient human pose models without accuracy performance compromise compared with existing model compression techniques such as network parameter binarisation.
- Extensive comparative evaluations on two human pose benchmarking datasets suggest that FPD is superior to a wide spectrum of state-of-the-art alternative methods.
- Ablation studies analyze model components and provide insight into the gains in model cost-effectiveness.