IEEE Trans Vis Comput Graph
December 2022
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous article, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data.
View Article and Find Full Text PDFA large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural network training, we contribute novel visualizations of high-dimensional control optimization landscapes; this yields insights into why control optimization is hard and why common practices like early termination and spline-based action parameterizations make optimization easier.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
August 2019
Efficient algorithms for 3D character control in continuous control setting remain an open problem in spite of the remarkable recent advances in the field. We present a sampling-based model-predictive controller that comes in the form of a Monte Carlo tree search (MCTS). The tree search utilizes information from multiple sources including two machine learning models.
View Article and Find Full Text PDFThe most time-consuming part of character animation is 3D character posing. Posing using a mouse is a slow and tedious task that involves sequences of selecting on-screen control handles and manipulating the handles to adjust character parameters, such as joint rotations and end effector positions. Thus, various 3D user interfaces have been proposed to make animating easier, but they typically provide less accuracy.
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