Skip to Main content Skip to Navigation
Journal articles

Depth-based hand pose estimation: methods, data, and challenges

Abstract : Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation (required for immersive virtual reality) and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
Document type :
Journal articles
Complete list of metadata

Cited literature [58 references]  Display  Hide  Download
Contributor : Gregory Rogez Connect in order to contact the contributor
Submitted on : Thursday, April 5, 2018 - 2:28:52 PM
Last modification on : Wednesday, November 3, 2021 - 7:19:49 AM


Files produced by the author(s)




James Steven Supancic, Gregory Rogez, Yi Yang, Jamie Shotton, Deva Ramanan. Depth-based hand pose estimation: methods, data, and challenges. International Journal of Computer Vision, Springer Verlag, 2018, 126 (11), pp.1180-1198. ⟨10.1007/s11263-018-1081-7⟩. ⟨hal-01759416⟩



Record views


Files downloads