The EISKO model package also included a whole face rig with blend shapes, suitable for changing the face shape by blending. The interpolation was driven by <a href="https://www.targetmol.com/compound/Dulcoside%20A">search Dulcoside A</a> control points equivalent to the ones in the monkey model, defined by the motioncaptured markers exploiting a ribbonlike structure that was inspired by the human muscle anatomy. Using a tension map algorithm, we determined the local deformations of the texture from the mesh deformations relative to the neutral pose.This package provides diffuse maps and highfrequency details as displacement maps <a href="https://pubmed.ncbi.nlm.nih.gov/34860003/">buy Dulcoside A</a> derived from a scanned real face.Final human avatar including hair animation.The monkey expressions were recorded from a yearold male animal.The expressions were elicited by showing the animal different objects, including a screw driver, a mirror, and an unknown male human individual.The animal was headfixed and observed the stimuli at a distance of cm in front of the camera setup.The recorded trajectories were segmented by a monkey expert, who had extensive experience with macaque monkeys on a daily basis.The human marker set was corresponding to the one of the monkey, except that it lacked markers on the ears. The human actor was instructed to show two facial expressions anger and fear.This functionality was not critical for the experiments presented in this paper, but it is used in ongoing experiments that build on the presented results.Graphical model showing the generative model underlying our motion morphing technique.Accuracy for the distinction between animations with original motion capture data and trajectories generated by our motion morphing algorithm is close to chance level, opposed to the accuracy for the detection of small motion artifacts in control stimuli, which was almost one for both avatar types.The two GPLVM layers reduce the dimensionality of the patterns in the highdimensional trajectory space in a nonlinear way.In our case, the dimensionality M of the hidden variables hn was six.The second layer of the model is defined exactly as the first layer.Here, the dimensionality of the variables hn is further reduced by generating a nonlinear mapping from the hidden state variables xn with Q dimensions, which defines the matrix X T.In addition, the kernel of this layer depends on the style vector variableseand s.This parametrization turns out to be favorable to separate the different style components and the motion content in the latent space, similar to multifactor models. We constrained the style vectors for all trials of the training data that represented the same motion style to be equal.In this way, the training data specify estimates e and e that correspond to averages of the expression types and, and estimates sM and sH that correspond to the average monkey and the human expressions.The style vectors for the motion morphs as functions of the style parameterseand s, as discussed in the main part of the paper, were given by the relationshipse ee e e ands ssM s sH, respectively.The highest level of the probabilistic model approximates the dynamics of the trajectories of the hidden state variables xn using a nonlinear extension of an autoregressive model, which is known as GPDM.
