Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users
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Abstract (arXiv)
The application of 2D markerless gait analysis has garnered increasing interest and application within clinical settings. However, its effectiveness in the realm of lower-limb amputees has remained less than optimal. In response, this study introduces an innovative zero-shot method employing image generation diffusion models to achieve markerless pose estimation for lower-limb prosthetics, presenting a promising solution to gait analysis for this specific population. Our approach demonstrates an enhancement in detecting key points on prosthetic limbs over existing methods, and enables clinicians to gain invaluable insights into the kinematics of lower-limb amputees across the gait cycle. The outcomes obtained not only serve as a proof-of-concept for the feasibility of this zero-shot approach but also underscore its potential in advancing rehabilitation through gait analysis for this unique population.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 52 references that are in the map. First posted on arXiv 13 Dec 2023.
Builds on
7 of 52 references.
References · 7
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2018Markerless tracking of user-defined features with deep learningcited by 206
- 2016DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Modelcited by 96
- 2020Monocular Human Pose Estimation: A Survey of Deep Learning-based Methodscited by 59
- 2020A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectivescited by 18
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Citing papers per year: 2025: 1.
1 paper.
- 2025AJAHR: Amputated Joint Aware 3D Human Mesh Recoverycited by 3
- 2025AJAHR: Amputated Joint Aware 3D Human Mesh Recovery24 Sep 2025
Similar, not cited
Close in wording, no citation either way
- 2026Diffusion-Based Generation of Gait Trajectories
- 2025KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture
- 2025Zero-shot Human Pose Estimation using Diffusion-based Inverse solvers
- 2024Diffusion Features for Zero-Shot 6DoF Object Pose Estimation
- 2024ZipGait: Bridging Skeleton and Silhouette with Diffusion Model for Advancing Gait Recognition
- 2025On Denoising Walking Videos for Gait Recognition
- 2026Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics
- 2026A Replay-Constrained Simulation Framework for Personalization of Powered Knee--Ankle Prosthesis Controllers