Examinando por Autor "Verschoor, Mickeal"

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Fine Virtual Manipulation with Hands of Different Sizes
(GMRV Publications, 2021) Sorli, Suzanne; Verschoor, Mickeal; Casas, Dan; Tajadura-Jiménez, Ana; Otaduy, Miguel A.
Natural interaction with virtual objects relies on two major technology components: hand tracking and hand-object physics simulation. There are functional solutions for these two components, but their hand representations may differ in size and skeletal morphology, hence making the connection non-trivial. In this paper, we introduce a pose retargeting strategy to connect the tracked and simulated hand representations, and we have formulated and solved this hand retargeting as an optimization problem. We have also carried out a user study that demonstrates the effectiveness of our approach to enable fine manipulations that are slow and awkward with na¨ıve approaches.
Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera
(ACM Transactions on Graphics, 2019) Mueller, Franziska; Davis, Micah; Bernard, Florian; Sotnychenko, Oleksandr; Verschoor, Mickeal; Otaduy, Miguel A.; Casas, Dan; Theobalt, Christian
Wepresentanovelmethodforreal-timeposeandshapereconstructionof twostronglyinteractinghands.Ourapproachisthefirsttwo-handtracking solutionthatcombinesanextensivelistoffavorableproperties,namelyitis marker-less,usesasingleconsumer-leveldepthcamera,runsinrealtime, handlesinter-andintra-handcollisions,andautomaticallyadjuststothe user’shandshape.Inordertoachievethis,weembedarecentparametric handposeandshapemodelandadensecorrespondencepredictorbasedon adeepneuralnetworkintoasuitableenergyminimizationframework.For trainingthecorrespondencepredictionnetwork,wesynthesizeatwo-hand dataset based on physical simulations that includes both hand pose and shapeannotationswhileatthesametimeavoidinginter-handpenetrations. Toachievereal-timerates,wephrasethemodelfittingintermsofanonlinear least-squaresproblemsothattheenergycanbeoptimizedbasedonahighly efficient GPU-based Gauss-Newton optimizer. We show state-of-the-art resultsinscenesthatexceedthecomplexityleveldemonstratedbyprevious
Tactile Rendering Based on Skin Stress Optimization
(Association for Computing Machinery (ACM), 2020) Verschoor, Mickeal; Casas, Dan; Otaduy, Miguel A.
We present a method to render virtual touch, such that the stimulus produced by a tactile device on a user’s skin matches the stimulus computed in a virtual environment simulation. To achieve this, we solve the inverse mapping from skin stimulus to device configuration thanks to a novel optimization algorithm. Within this algorithm, we use a device-skin simulation model to estimate rendered stimuli, we account for trajectory-dependent effects efficiently by decoupling the computation of the friction state from the optimization of device configuration, and we accelerate computations using a neural-network approximation of the device-skin model. Altogether, we enable real-time tactile rendering of rich interactions including smooth rolling, but also contact with edges, or frictional stick-slip motion. We validate our algorithm both qualitatively through user experiments, and quantitatively on a BioTac biomimetic finger sensor.