Self-Supervised Hand Pose Estimation

Investigator: Angela Yao.

We aim to bridge data-driven discriminative hand pose estimation with optimization-based model-fitting.  Using a differentiable hand renderer that aligns estimates by comparing the rendered and input depth maps, our method’s accuracy improves with the amount of data encountered, while not needing human-provided annotations.



Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from Youtube
Consent to display content from Vimeo
Google Maps
Consent to display content from Google