Computer Graphics Term Papers

Computer Graphics Term Papers-27
Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions.The linear deformations are computed directly from the transformations of the rig's underlying skeleton.They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance.

Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions.The linear deformations are computed directly from the transformations of the rig's underlying skeleton.They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance.

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We use deep learning methods to approximate the remaining nonlinear portion.

In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5x-10x.

We show the versatility of our method by capturing a variety of materials with both one and two camera input streams and rendering our results on 3D objects under complex illumination.

The growing ease with which digital images can be convincingly manipulated and widely distributed on the Internet makes viewers increasingly susceptible to visual misinformation and deception.

The results are in good qualitative agreement with natural joint patterns.

We then apply the method to a set of parallel veins and demonstrate how the variations in thickness of the veins can be represented.

The modelled fault network shows both radial and concentric faults.

The new method provides an effective means of modelling joint and fault networks that can be imported to the flow simulator.

Viewers’ attitude toward a depicted issue also positively influenced their credibility evaluation.

Character rigs are procedural systems that compute the shape of an animated character for a given pose.

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