Performance timing for keyframe animation

Terra, S. C. L.; Metoyer, Ronald

doi:10.1145/1028523.1028556

Cited by 39 publications

(13 citation statements)

References 11 publications

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“…Layering time-sequential performances allows for rich animations even from mouse and keyboard input [Dontcheva et al 2003;Neff et al 2007;Martin and Neff 2012]. Acting out animations proves useful for animation timing [Terra and Metoyer 2004], but control becomes difficult for complex characters and motions.…”

Section: Related Workmentioning

confidence: 99%

Generalizing wave gestures from sparse examples for real-time character control

Rhodin¹,

Tompkin²,

Kim

et al. 2015

ACM Trans. Graph.

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Figure 1: Top: Skeletal, facial, and hand motions are tracked by off-the-shelf sensors. From sparse user-defined examples, our method reliably separates and extrapolates simultaneously-performed gestures to control characters in games or VR. Bottom left: Dog happy walk, neutral run, shaking, and sitting. Bottom right: Caterpillar crawl variations, controlled by varying amplitude, phase, and frequency of input gestures. AbstractMotion-tracked real-time character control is important for games and VR, but current solutions are limited: retargeting is hard for nonhuman characters, with locomotion bound to the sensing volume; and pose mappings are ambiguous with difficult dynamic motion control. We robustly estimate wave properties -amplitude, frequency, and phase -for a set of interactively-defined gestures by mapping user motions to a low-dimensional independent representation. The mapping separates simultaneous or intersecting gestures, and extrapolates gesture variations from single training examples. For animations such as locomotion, wave properties map naturally to stride length, step frequency, and progression, and allow smooth transitions from standing, to walking, to running. Interpolating outof-phase locomotions is hard, e.g., quadruped legs between walks and runs switch phase, so we introduce a new time-interpolation scheme to reduce artifacts. These improvements to real-time motiontracked character control are important for common cyclic animations. We validate this in a user study, and show versatility to apply to part-and full-body motions across a variety of sensors.

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Section: Related Workmentioning

confidence: 99%

Generalizing wave gestures from sparse examples for real-time character control

Rhodin¹,

Tompkin²,

Kim

et al. 2015

ACM Trans. Graph.

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“…Keyframing methods can also be used to assist novice users to animate characters in 3D. 5,15 In a related approach, McCann and Pollard 16 proposed an animation controller that synthesizes motion sequences from motion fragments.…”

Section: Related Workmentioning

confidence: 99%

“…Existing sketch-based interfaces allow users to draw the path to be taken by a character [1][2][3] or the trajectory of a rigid body. 4,5 These approaches offer relatively accessible interfaces, but do not permit detailed 3D motions to be created without the use of a 3D input device such as a tracker. An exception is the Motion Doodles system, 6 which does allow a user to create the motion of a character, in both 2D and 3D, using cursive gestures within a one-pass sketch-based interface.…”

Section: Introductionmentioning

confidence: 99%

A sketch interface to empower novices to create 3D animations

Jeon

Jang²,

Lim³

et al. 2010

Computer Animation & Virtual

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We introduce a multiple-pass sketch-based user interface, designed for novice users, which avoids spatial ambiguities and hence allows considerable freedom in creating 3D animations. The user first draws the path along which a character or object is to move. A vertical motion window is then erected along this path, on which the user specifies the character's gestures or the object's vertical movements. A further cross-motion window can be used to add detail to the movements of the object. Window-specific camera modules present the user with appropriate views, extending the fluidity of pencil and paper to 3D. A usability study involving 60 users, who were previously unfamiliar with 3D animation tools, demonstrated that the proposed interface is easy to learn and effective in use.

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“…In [1], Terra and Metroyer proposed a solution to find the timings of the keyframes. They have used a performance based approach.…”

Section: Introductionmentioning

confidence: 99%

Keyframe Reduction Techniques for Motion Capture Data

Önder

Güdükbay

Özgüç

et al. 2008

2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video

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Two methods for keyframe reduction of motion capture data are presented. Keyframe reduction of motion capture data enables animators to easily edit motion data with smaller number of keyframes. One of the approaches achieves keyframe reduction and noise removal simultaneously by fitting a curve to the motion information using dynamic programming. The other approach uses curve simplification algorithms on the motion capture data until a predefined threshold of number of keyframes is reached. Although the error rate varies with different motions, the results show that curve fitting with dynamic programming performs as good as curve simplification methods.

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Performance timing for keyframe animation

Cited by 39 publications

References 11 publications

Generalizing wave gestures from sparse examples for real-time character control

Generalizing wave gestures from sparse examples for real-time character control

A sketch interface to empower novices to create 3D animations

Keyframe Reduction Techniques for Motion Capture Data

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