Markerless Motion Capture
Interacting Hands with Objects
Capturing the motion of two hands interacting with an object is a very challenging task due to the large number of degrees of freedom, self-occlusions, and similarity between the fingers, even in the case of multiple views observing the scene. In this paper we propose to use discriminatively learned salient points on the fingers and to estimate the finger-salient point associations and the hand poses at the same time. We introduce a differentiable objective function that also takes edges, optical flow and collisions into account.
Datasets and Results: | [link] |
Code: | [link] |
Results:
Publications:
Motion Capture of Hands in Action using Discriminative Salient PointsL. Ballan, A. Taneja, J. Gall, L. Van Gool, M. Pollefeys [PDF] [web] [bibtex] [supp] [dataset] (Oral)ECCV 2012, Firenze, Italy |
Capturing Hands in Action using Discriminative Salient Points and Physics SimulationD. Tzionas, L. Ballan, A. Srikantha, P. Aponte, M. Pollefeys, J. Gall [arXiv pre-print] [video] |
Interacting People
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Input: 4 Video Streams | Output: Pose Estimation |
Introductory video: |
Results:
Soccer Juggling | Soccer Passing Drill | Two People Boxing | Handstand | Pirouettes |
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Handshake | Somersault (result overlaid) |
Sword Swing | Simple Break- Dancing |
Datasets and Results: | [link] |
Code: | [link] |
Publications:
Marker-less Motion Capture of Skinned Models in a Four Camera Set-up using Optical Flow and SilhouettesL. Ballan and G. M. Cortelazzo [PDF] [video] [bibtex] [dataset](Best Paper Award)
3DPVT 2008, Atlanta, GA, USA |
Acquiring Shape and Motion of Interacting People from VideosL. Ballan [PDF] [video] [bibtex] [dataset]PhD's thesis, Department of Information Engineering, University of Padova, 2009 |