SCA 2023: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/3543889

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Browsing SCA 2023: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "based simulation"

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    Micropolar Elasticity in Physically-Based Animation
    (ACM Association for Computing Machinery, 2023) Löschner, Fabian; Fernández-Fernández, José Antonio; Jeske, Stefan Rhys; Longva, Andreas; Bender, Jan; Wang, Huamin; Ye, Yuting; Victor Zordan
    We explore micropolar materials for the simulation of volumetric deformable solids. In graphics, micropolar models have only been used in the form of one-dimensional Cosserat rods, where a rotating frame is attached to each material point on the one-dimensional centerline. By carrying this idea over to volumetric solids, every material point is associated with a microrotation, an independent degree of freedom that can be coupled to the displacement through a material’s strain energy density. The additional degrees of freedom give us more control over bending and torsion modes of a material. We propose a new orthotropic micropolar curvature energy that allows us to make materials stiff to bending in specific directions. For the simulation of dynamic micropolar deformables we propose a novel incremental potential formulation with a consistent FEM discretization that is well suited for the use in physically-based animation. This allows us to easily couple micropolar deformables with dynamic collisions through a contact model inspired from the Incremental Potential Contact (IPC) approach. For the spatial discretization with FEM we discuss the challenges related to the rotational degrees of freedom and propose a scheme based on the interpolation of angular velocities followed by quaternion time integration at the quadrature points. In our evaluation we validate the consistency and accuracy of our discretization approach and demonstrate several compelling use cases for micropolar materials. This includes explicit control over bending and torsion stiffness, deformation through prescription of a volumetric curvature field and robust interaction of micropolar deformables with dynamic collisions.
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    Physics-based Motion Retargeting from Sparse Inputs
    (ACM Association for Computing Machinery, 2023) Reda, Daniele; Won, Jungdam; Ye, Yuting; Panne, Michiel van de; Winkler, Alexander; Wang, Huamin; Ye, Yuting; Victor Zordan
    Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user’s motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user’s pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time.We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human.We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.