229 research outputs found
Neural Volumetric Blendshapes: Computationally Efficient Physics-Based Facial Blendshapes
Computationally weak systems and demanding graphical applications are still
mostly dependent on linear blendshapes for facial animations. The accompanying
artifacts such as self-intersections, loss of volume, or missing soft tissue
elasticity can be avoided by using physics-based animation models. However,
these are cumbersome to implement and require immense computational effort. We
propose neural volumetric blendshapes, an approach that combines the advantages
of physics-based simulations with realtime runtimes even on consumer-grade
CPUs. To this end, we present a neural network that efficiently approximates
the involved volumetric simulations and generalizes across human identities as
well as facial expressions. Our approach can be used on top of any linear
blendshape system and, hence, can be deployed straightforwardly. Furthermore,
it only requires a single neutral face mesh as input in the minimal setting.
Along with the design of the network, we introduce a pipeline for the
challenging creation of anatomically and physically plausible training data.
Part of the pipeline is a novel hybrid regressor that densely positions a skull
within a skin surface while avoiding intersections. The fidelity of all parts
of the data generation pipeline as well as the accuracy and efficiency of the
network are evaluated in this work. Upon publication, the trained models and
associated code will be released
The Diamond Laplace for polygonal and polyhedral meshes
We introduce a construction for discrete gradient operators that can be directly applied to arbitrary polygonal surface as well as polyhedral volume meshes. The main idea is to associate the gradient of functions defined at vertices of the mesh with diamonds: the region spanned by a dual edge together with its corresponding primal element — an edge for surface meshes and a face for volumetric meshes. We call the operator resulting from taking the divergence of the gradient Diamond Laplacian. Additional vertices used for the construction are represented as affine combinations of the original vertices, so that the Laplacian operator maps from values at vertices to values at vertices, as is common in geometry processing applications. The construction is local, exactly the same for all types of meshes, and results in a symmetric negative definite operator with linear precision. We show that the accuracy of the Diamond Laplacian is similar or better compared to other discretizations. The greater versatility and generally good behavior come at the expense of an increase in the number of non-zero coefficients that depends on the degree of the mesh elements
Применение сервис-ориентированной архитектуры при интеграции систем управления технологическими процессами
Отражен опыт применения сервис-ориентированной архитектуры при создании автоматизированных систем управления технологическими процессами и их интеграции на ОАО "НПК "Уралвагонзавод"
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction
Göpfert JP, Göpfert C, Botsch M, Hammer B. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2017
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Multi-Scale Capture of Facial Geometry and Motion
We present a novel multi-scale representation and acquisition method for the animation of high-resolution facial geometry and wrinkles. We first acquire a static scan of the face including reflectance data at the highest possible quality. We then augment a traditional marker-based facial motion-capture system by two synchronized video cameras to track expression wrinkles. The resulting model consists of high-resolution geometry, motion-capture data, and expression wrinkles in 2D parametric form. This combination represents the facial shape and its salient features at multiple scales. During motion synthesis the motion-capture data deforms the high-resolution geometry using a linear shell-based mesh-deformation method. The wrinkle geometry is added to the facial base mesh using nonlinear energy optimization. We present the results of our approach for performance replay as well as for wrinkle editing.Engineering and Applied Science
Accurate online alignment of human motor performances
Hülsmann F, Richter A, Kopp S, Botsch M. Accurate online alignment of human motor performances. In: Proceedings of ACM Motion in Games. Barcelona: ACM; 2017: pp. 7:1-7:6
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