SCA 15: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/14634

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Browsing SCA 15: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "animation"

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    Computational Design of Walking Automata
    (ACM Siggraph, 2015) Bharaj, Gaurav; Coros, Stelian; Thomaszewski, Bernhard; Tompkin, James; Bickel, Bernd; Pfister, Hanspeter; Florence Bertails-Descoubes and Stelian Coros and Shinjiro Sueda
    Creating mechanical automata that can walk in stable and pleasing manners is a challenging task that requires both skill and expertise. We propose to use computational design to offset the technical difficulties of this process. A simple drag-and-drop interface allows casual users to create personalized walking toys from a library of pre-defined template mechanisms. Provided with this input, our method leverages physical simulation and evolutionary optimization to refine the mechanical designs such that the resulting toys are able to walk. The optimization process is guided by an intuitive set of objectives that measure the quality of the walking motions. We demonstrate our approach on a set of simulated mechanical toys with different numbers of legs and various distinct gaits. Two fabricated prototypes showcase the feasibility of our designs.
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    Learning an Inverse Rig Mapping for Character Animation
    (ACM Siggraph, 2015) Holden, Daniel; Saito, Jun; Komura, Taku; Florence Bertails-Descoubes and Stelian Coros and Shinjiro Sueda
    We propose a general, real-time solution to the inversion of the rig function - the function which maps animation data from a character's rig to its skeleton. Animators design character movements in the space of an animation rig, and a lack of a general solution for mapping motions from the skeleton space to the rig space keeps the animators away from the state-of-the-art character animation methods, such as those seen in motion editing and synthesis. Our solution is to use non-linear regression on sparse example animation sequences constructed by the animators, to learn such a mapping offline. When new example motions are provided in the skeleton space, the learned mapping is used to estimate the rig space values that reproduce such a motion. In order to further improve the precision, we also learn the derivative of the mapping, such that the movements can be fine-tuned to exactly follow the given motion. We test and present our system through examples including full-body character models, facial models and deformable surfaces. With our system, animators have the freedom to attach any motion synthesis algorithms to an arbitrary rigging and animation pipeline, for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.