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Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.02 vteřin. 
Blender Add-on For Conversion of Nodes to Vector Graphics
 Dráber, Filip ; Milet, Tomáš (oponent) ; Chlubna, Tomáš (vedoucí práce)
This project proposes and implements a Blender add-on which lets the user export on-demand vector representations of the Node graphs they have created within the editor. The technical report delves into the technical aspects of both Blender's internal representation as well as features of the Scalable Vector Graphics format (SVG) which are used to produce the vector representation. It showcases how the add-on describes various Node types and streamlines their representation. Finally, it evaluates the usability and performance of the add-on, which has already been used in an academic environment at time of writing.
Úplný záznam
Generating Animations with Neural Networks
 Dráber, Filip ; Kohút, Jan (oponent) ; Hradiš, Michal (vedoucí práce)
While motion capture serves as a mean for animators to circumvent some of the most arduous aspects of creating realistic animation, there is still a lot of work hiding in annotating and structuring the data. I solve this problem by designing a neural network which can be trained on a motion capture data file to reproduce human locomotion visualized in an application which allows for the user to control the character's direction. I also subject various methods of training an autoregressive model to experiments and find which method trades training times for performance the best. Additionally, I remark how the addition of certain control features to frame-by-frame generations impacts the use of recurrent neural networks for this task.
Úplný záznam
Generating Animations with Neural Networks
 Dráber, Filip ; Kohút, Jan (oponent) ; Hradiš, Michal (vedoucí práce)
While motion capture serves as a mean for animators to circumvent some of the most arduous aspects of creating realistic animation, there is still a lot of work hiding in annotating and structuring the data. I solve this problem by designing a neural network which can be trained on a motion capture data file to reproduce human locomotion visualized in an application which allows for the user to control the character's direction. I also subject various methods of training an autoregressive model to experiments and find which method trades training times for performance the best. Additionally, I remark how the addition of certain control features to frame-by-frame generations impacts the use of recurrent neural networks for this task.
Úplný záznam

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