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Figure 1: Our approach transfers the animation setup from a rigged source character to target character meshes. Using a geometric correspondence, it retargets the skeleton and the skinning weights to animate the target static meshes. AbstractWe present a general method for transferring skeletons and skinning weights between characters with distinct mesh topologies. Our pipeline takes as inputs a source character rig (consisting of a mesh, a transformation hierarchy of joints, and skinning weights) and a target character mesh. From these inputs, we compute joint locations and orientations that embed the source skeleton in the target mesh, as well as skinning weights to bind the target geometry to the new skeleton. Our method consists of two key steps. We first compute the geometric correspondence between source and target meshes using a semi-automatic method relying on a set of markers. The resulting geometric correspondence is then used to formulate attribute transfer as an energy minimization and filtering problem. We demonstrate our approach on a variety of source and target bipedal characters, varying in mesh topology and morphology. Several examples demonstrate that the target characters behave well when animated with either forward or inverse kinematics. Via these examples, we show that our method preserves subtle artistic variations; spatial relationships between geometry and joints, as well as skinning weight details, are accurately maintained. Our proposed pipeline opens up many exciting possibilities to quickly animate novel characters by reusing existing production assets.

In this paper we present a new technique to dynamically adapt the first step (broad phase) of the collision detection process on hardware architecture during simulation. Our approach enables to face the unpredictable evolution of the simulation scenario (this includes addition of complex objects, deletion, split into several objects, ...). Our technique of dynamic adaptation is performed on sequential CPU, multi-core, single GPU and multi-GPU architectures. We propose to use off-line simulations to determine fields of optimal performance for broad phase algorithms and use them during in-line simulation. This is achieved by a features analysis of algorithmic performances on different architectures. In this way we ensure the real time adaptation of the broad-phase algorithm during the simulation, switching it to a more appropriate candidate. We also present a study on how graphics hardware parameters (number of cores, bandwidth, ...) can influence algorithmic performance. The goal of this analysis is to know if it is possible to find a link between variations of algorithms performances and hardware parameters. We test and compare our model on 1,2, 4 and 8 cores architectures and also on 1 Quadro FX 3600M, 2 Quadro FX 4600 and 4 Quadro FX 5800. Our results show that using this technique during the collision detection process provides better performance throughout the simulation and enables to face unpredictable scenarios evolution in large-scale virtual environments.

Even if the appearance and geometry of the human eye have been extensively studied during the last decade, the geometrical correlation between gaze direction, eyelids aperture and eyelids shape has not been empirically modeled. In this paper, we propose a data‐driven approach for capturing and modeling the subtle features of the human eye region, such as the inner eye corner and the skin bulging effect due to globe orientation. Our approach consists of an original experimental setup to capture the eye region geometry variations combined with a 3D reconstruction method. Regarding the eye region capture, we scanned 55 participants doing 36 eyes poses. To animate a participant's eye region, we register the different poses to a vertex wise correspondence before blending them in a trilinear fashion. We show that our 3D animation results are visually pleasant and realistic while bringing novel eye features compared to state of the art models.