In this paper we present an intuitive tool suitable for 2D artists using touch-enabled pen tablets. An artist-oriented tool should be easy-to-use, real-time, versatile, and locally refinable. Our approach uses an interactive system for 3D character posing from 2D strokes. We employ a closed-form solution for the 2D strokes to 3D skeleton registration problem. We first construct an intermediate 2D stroke representation by extracting local features using meaningful heuristics. Then, we match 2D stroke segments to 3D bones. Finally, 3D bones are carefully realigned with the matched 2D stroke segments while enforcing important constraints such as bone rigidity and depth. Our technique is real-time and has a linear time complexity. It is versatile, as it works with any type of 2D stroke and 3D skeleton input. Finally, thanks to its coarse-to-fine design, it allows users to perform local refinements and thus keep full control over the final results. We demonstrate that our system is suitable for 2D artists using touch-enabled pen tablets by posing 3D characters with heterogeneous topologies (bipeds, quadrupeds, hands) in real-time.

In this article, we present a parallel prioritized Jacobian-based inverse kinematics algorithm for multithreaded architectures. We solve damped least squares inverse kinematics using a parallel line search by identifying and sampling critical input parameters. Parallel competing execution paths are spawned for each parameter in order to select the optimum that minimizes the error criteria. Our algorithm is highly scalable and can handle complex articulated bodies at interactive frame rates. We show results on complex skeletons consisting of more than 600 degrees of freedom while being controlled using multiple end effectors. We implement the algorithm both on multicore and GPU architectures and demonstrate how the GPU can further exploit fine-grain parallelism not directly available on a multicore processor. Our implementations are 10 to 150 times faster compared to a state-of-the-art serial implementation while providing higher accuracy. We also demonstrate the scalability of the algorithm over multiple scenarios and explore the GPU implementation in detail.

We present a new approach for whole-body motion synthesis that is able to generate high-quality motions for challenging mobile-manipulation scenarios. Our approach decouples the problem in specialized locomotion and manipulation skills, and proposes a multi-modal planning scheme that explores the search space of each skill together with the possible transition points between skills. In order to achieve high-quality results the locomotion skill is designed to be fully data-driven, while manipulation skills can be algorithmic or data-driven according to data availability and the complexity of the environment. Our method is able to automatically generate complex motions with precise manipulation targets among obstacles and in coordination with locomotion.

We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.

We introduce an approach for enabling sampling-based planners to compute motions with humanlike appearance. The proposed method is based on a space of blendable example motions collected by motion capture. This space is explored by a sampling-based planner that is able to produce motions around obstacles while keeping solutions similar to the original examples. The results therefore largely maintain the humanlike characteristics observed in the example motions. The method is applied to generic upper-body actions and is complemented by a locomotion planner that searches for suitable body placements for executing upper-body actions successfully. As a result, our overall multi-modal planning method is able to automatically coordinate whole-body motions for action execution among obstacles, and the produced motions remain similar to example motions given as input to the system.