Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
This work addresses a significant challenge in hand motion capture for scenarios involving interactions, which is incremental as it builds on existing single-hand methods.
The paper tackles the problem of hand motion capture during interactions with other hands or objects, presenting a framework that combines discriminative salient points and physics simulation to achieve low tracking error and physically plausible estimates, even under occlusions.
Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.