LibEE

Watt, Martin; Cutler, Lawrence D.; Powell, Alex; Duncan, Brendan; Hutchinson, Michael F.; Ochs, Kevin

doi:10.1145/2370919.2370930

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…Despite the performance gains from evaluating multiple input poses simultaneously, we timed the approximation models evaluating inputs one at a time to demonstrate the applicability of our method for interactive applications. In Table 5, we compare the runtime of our method with Libee [Watt et al 2012], a highly optimized, multi-threaded rig evaluation engine, on four different character rigs. All of the character rigs have been optimized to evaluate as fast as possible in Libee.…”

Section: Model Speedmentioning

confidence: 99%

Fast and deep deformation approximations

et al. 2018

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Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. 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. 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. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device.

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Section: Model Speedmentioning

confidence: 99%

Fast and deep deformation approximations

et al. 2018

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“…We compare the timing of our approximation against the original rig evaluation software for Hiccup, Valka, and Toothless. These three character rigs are designed for Libee [Watt et al 2012], a multithreaded rig evaluation engine. Character artists optimized these rigs to run as fast as possible on the engine.…”

Section: Timingmentioning

confidence: 99%

Fast and deep facial deformations

et al. 2020

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Film-quality characters typically display highly complex and expressive facial deformation. The underlying rigs used to animate the deformations of a character's face are often computationally expensive, requiring high-end hardware to deform the mesh at interactive rates. In this paper, we present a method using convolutional neural networks for approximating the mesh deformations of characters' faces. For the models we tested, our approximation runs up to 17 times faster than the original facial rig while still maintaining a high level of fidelity to the original rig. We also propose an extension to the approximation for handling high-frequency deformations such as fine skin wrinkles. While the implementation of the original animation rig depends on an extensive set of proprietary libraries making it difficult to install outside of an in-house development environment, our fast approximation relies on the widely available and easily deployed TensorFlow libraries. In addition to allowing high frame rate evaluation on modest hardware and in a wide range of computing environments, the large speed increase also enables interactive inverse kinematics on the animation rig. We demonstrate our approach and its applicability through interactive character posing and real-time facial performance capture.

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Premo: DreamWorks Animation's New Approach to Animation

DiLorenzo

2015

IEEE Comput. Grap. Appl.

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LibEE

Cited by 13 publications

References 10 publications

Fast and deep deformation approximations

Fast and deep deformation approximations

Fast and deep facial deformations

Premo: DreamWorks Animation's New Approach to Animation

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