Robust realtime physics-based motion control for human grasping

Zhao, Wei; Zhang, Jianjie; Min, Jianyuan; Chai, Jinxiang

doi:10.1145/2508363.2508412

Cited by 90 publications

(46 citation statements)

References 24 publications

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“…Hand animation for virtual characters can be reconstructed from image based [Zhao et al 2012;, marker based [Kang et al 2012;Wheatland et al 2013], and glove based [Wang and Neff 2013;Huenerfauth and Lu 2010;Griffin et al 2000;Hu et al 2004;Menon et al 2003;Steffen et al 2011] motion capture techniques, or synthesized using physics knowledge [Zhao et al 2013;Liu 2008;2009;Ye and Liu 2012], data-driven [Jörg et al 2012;ElKoura and Singh 2003] or rule-based methods [Zhu et al 2012]. However, despite all the hand motion acquisition and generation research, little hand motion perception work has been conducted.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Impact of Hand Motion on Virtual Character Personality

Wang

Tree

Walker

et al. 2016

ACM Trans. Appl. Percept.

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Designing virtual characters that are capable of conveying a sense of personality is important for generating realistic experiences, and thus a key goal in computer animation research. Though the influence of gesture and body motion on personality perception has been studied, little is known about which attributes of hand pose and motion convey particular personality traits. Using the "Big Five" model as a framework for evaluating personality traits, this work examines how variations in hand pose and motion impact the perception of a character's personality. As has been done with facial motion, we first study hand motion in isolation as a requirement for running controlled experiments that avoid the combinatorial explosion of multimodal communication (all combinations of facial expressions, arm movements, body movements and hands) and allow us to understand the communicative content of hands. We determined a set of features likely to reflect personality, based on research in psychology and previous human motion perception work: shape, direction, amplitude, speed and manipulation. Then we captured realistic hand motion varying these attributes, and conducted three perceptual experiments to determine the contribution of these attributes to the character's personalities. Both hand poses and the amplitude of hand motion affected the perception of all five personality traits. Speed impacted all traits except openness. Direction impacted extraversion and openness. Manipulation was perceived as an indicator of introversion, disagreeableness, neuroticism and less openness to experience. From these results, we generalize guidelines for designing detailed hand motion that can add to the expressiveness and personality of characters. We performed an evaluation study that combined hand motion with gesture and body motion. Even in the presence of body motion, hand motion still significantly impacted the perception of a character's personality and could even be the dominant factor in certain situations.

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Section: Related Workmentioning

confidence: 99%

Assessing the Impact of Hand Motion on Virtual Character Personality

Wang

Tree

Walker

et al. 2016

ACM Trans. Appl. Percept.

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“…After extraction to motion features, the next job is to identify corresponding actions which contains characteristics of these movements. In order to satisfy the visual rationality, the system can generate the action which conforms to the physical law [3][4][5]. Sequence of actions can be considered as trajectory in the model parameter space, and each different action category can be grouped into a subset of the model parameter space.…”

Section: Related Workmentioning

confidence: 99%

A Somatosensory Interaction System based on Kinect

Liang

Wang

2016

MATEC Web of Conferences

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Abstract. The somatosensory interaction technique is one form of the perceptual user interface which is used in video game and virtual reality more and more widly. In this paper, a somatosensory interaction system based on Kinect is presented. Firstly, the user performances his action in front of a kinect, the sensing data from kinect is preprocessed and the main features of the action are extracted. Secondly, the performaced action is recognized by the matching algorithm based on Dynamic Time Warping Hidden Markov Model. Finally, the recognized motion is employed to interact with the virtual human and virtual environment. A series of experiments have been done to test the availablity of our system. Results show that the recognition rate is high enough to be used in virtual reality applications.

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“…Recently, ] framed the problem of capturing high-contact sequences from a multi-camera video stream as an offline optimization using a physics-based motion controller. [Zhao et al 2013] used a combination of kinematic reference posing and physics-driven control to create rich motions for a human hand. A common limitation of such motion capture methods is they can not be easily retargeted to different hand morphologies (e.g., to a three-fingered claw as in Fig.…”

Section: Related Workmentioning

confidence: 99%

Hands on

Humberston

Pai

2015

Proceedings of the 14th ACM SIGGRAPH / Eurographics Symposium on Computer Animation

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a) Interface (b) Wine Glass (c) Champagne (d) Chess (e) Cigarette (f) Robot claw Figure 1: Interactively creating movements of animated hands using our interface. (a) A typical desktop setup, including haptic interfaces and stereo display. (b,c) Grasps conform to object shapes using our adaption method. (d,e) Dynamic interaction with grasped objects and the environment. (f) Generalization: we can easily control a non-anthropomorphic robotic hand with the same interface. The example also shows stacking virtual objects with compliant contact. AbstractHumans show effortless dexterity while manipulating objects using their own hands. However, specifying the motion of a virtual character's hand or of a robotic manipulator remains a difficult task that requires animation expertise or extensive periods of offline motion capture. We present Hands On: a real-time, adaptive animation interface, driven by compliant contact and force information, for animating contact and precision manipulations of virtual objects. Using our interface, an animator controls an abstract grasper trajectory while the full hand pose is automatically shaped by proactive adaptation and compliant scene interactions. Haptic force feedback enables intuitive control by mapping interaction forces from the full animated hand back to the reduced animator feedback space, invoking the same human sensorimotor processes utilized in natural precision manipulations. We provide an approach for online, adaptive shaping of the animated manipulator based on prior interactions, resulting in more functional and appealing motions. The importance of haptic feedback for authoring virtual object manipulations is verified in a user study with nonexpert participants that examines contact force trajectories while using our interface. Comparing the quality of motions produced with and without force rendering, haptic feedback is shown to be critical for efficiently communicating contact forces and dynamic events to the user.

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Robust realtime physics-based motion control for human grasping

Cited by 90 publications

References 24 publications

Assessing the Impact of Hand Motion on Virtual Character Personality

Assessing the Impact of Hand Motion on Virtual Character Personality

A Somatosensory Interaction System based on Kinect

Hands on

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