Reconstructing whole-body motions with wrist trajectories

Reconstructing whole-body motions using only a low-dimensional input reduces the cost of and efforts for performance capture significantly, and yet remains a challenging problem. We introduce a novel technique that synthesizes whole-body motion using the two wrist trajectories. Given the wrist trajectories, we first determine the optimal ankle trajectories from a large number of candidate ankle paths obtained from example poses in the motion database. The optimal trajectory is efficiently achieved by solving for the shortest path problem in a directed acyclic graph. Next, we use both the wrist and ankle trajectories as the low-dimensional control signals to achieve the whole-body pose at each time step. We show that our method can reconstruct various whole-body motions that can be recognized by arm motions, such as walking, stepping, and in-place upper-body motions. Comparisons with ground truth motions and with other methods are provided.

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