Motion fields for interactive character locomotion

Lee, Young Hee; Wampler, Kevin; Bernstein, Gilbert; Popović, Zoran

doi:10.1145/1866158.1866160

Cited by 50 publications

(34 citation statements)

References 35 publications

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“…Our concept of a gesture space is similar to the idea of Motion Fields [LWB*14], in which the authors propose a high‐dimensional continuous space that incorporates the set of all possible motion states in character motion. However, unlike character motion, the way a user performs a particular gesture can vary significantly from person to person.…”

Section: Gesture Spacementioning

confidence: 99%

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Shen

Henry

Wang

et al. 2018

Computer Graphics Forum

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Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.

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Section: Gesture Spacementioning

confidence: 99%

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Shen

Henry

Wang

et al. 2018

Computer Graphics Forum

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“…In this section we brie y review reinforcement learning methods, sampling based controllers and various approaches for synthesizing physically based locomotion for animated characters. We focus on approaches that simulate or otherwise handle full dynamics, as opposed to primarily kinematic approaches such as Motion Graphs [Kovar et al 2002] and Motion Fields [Lee et al 2010], which do not handle the environment's dynamics and can perform unphysical movements.…”

Section: Related Workmentioning

confidence: 99%

Augmenting sampling based controllers with machine learning

Rajamäki

Hämäläinen

2017

Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation

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“…The user could draw various movements and moving paths, such as normal walking, sneaking, marching, and jumping, with an electronic pen. In , a character was created that interacted with the user input more rapidly and effectively by using reinforcement learning. Levine, et al .…”

Section: Related Workmentioning

confidence: 99%

Let's be a virtual juggler

Choi

Kim

et al. 2016

Computer Animation & Virtual

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Juggling, which uses both hands to keep several objects in the air at once, is admired by anyone who sees it. However, skillful real‐world juggling requires long, hard practice. Therefore, we propose an interesting method to enable anyone to juggle skillfully in the virtual world. In the real world, the human motion has to follow the motion of the moving objects; in the virtual world, the objects' motion can be adjusted together with the human motion. By using this freedom, we have generated a juggling avatar that can follow the user's motion. The user simply makes juggling‐like motions in front of a motion sensor. Our system then searches for juggling motions that closely match the user's motions and connects them smoothly. We then generate moving objects that both satisfy the laws of physics and are synchronized with the synthesized motion of the avatar. In this way, we can generate a variety of juggling animations by an avatar in real time. Copyright © 2016 John Wiley & Sons, Ltd.

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Motion fields for interactive character locomotion

Cited by 50 publications

References 35 publications

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Augmenting sampling based controllers with machine learning

Let's be a virtual juggler

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