Relationship descriptors for interactive motion adaptation

Al-Asqhar, Rami Ali; Komura, Taku; Choi, Myung Geol

doi:10.1145/2485895.2485905

Cited by 57 publications

(46 citation statements)

References 37 publications

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“…The result generated by our method (Figure 7 (c)) shows the body parts are coordinated such as the knees) to keep the COP to be lying over the support polygon. The results highlight the advantage of our proposed method over another real-time motion adaptation approach [Al-Asqhar et al 2013] in which physical constraints can be enforced in our method to produce physically feasible motions. The readers are referred to the attached video demo to evaluate the quality of the retargeted motions.…”

Section: Motion Adaptation With Body Balance Constraintsmentioning

confidence: 81%

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A multi-resolution approach for adapting close character interaction

Wang

Komura

2014

Proceedings of the 20th ACM Symposium on Virtual Reality Software and Technology

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Synthesizing close interactions such as dancing and fighting between characters is a challenging problem in computer animation. While encouraging results are presented in [Ho et al. 2010], the high computation cost makes the method unsuitable for interactive motion editing and synthesis. In this paper, we propose an efficient multiresolution approach in the temporal domain for editing and adapting close character interactions based on the Interaction Mesh framework. In particular, we divide the original large spacetime optimization problem into multiple smaller problems such that the user can observe the adapted motion while playing-back the movements during run-time. Our approach is highly parallelizable, and achieves high performance by making use of multi-core architectures. The method can be applied to a wide range of applications including motion editing systems for animators and motion retargeting systems for humanoid robots.

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Section: Motion Adaptation With Body Balance Constraintsmentioning

confidence: 81%

“…However, existing methods except either cannot handle close interactions well, or even they do, their convergences is either slow (such as [Ho et al 2010]) or have issues handling physical constraints (such as [Al-Asqhar et al 2013]) which can be important for some applications.…”

Section: Related Workmentioning

confidence: 99%

A multi-resolution approach for adapting close character interaction

Wang

Komura

2014

Proceedings of the 20th ACM Symposium on Virtual Reality Software and Technology

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“…5 we show the application of motion editing using our technique. We synthesize some locomotion using animator supplied data and use Spatial Descriptors [Al-Asqhar et al 2013] to edit the result. This advanced motion editing technique expresses an animation in terms of its environment which allows an animation to be naturally deformed to follow some terrain.…”

Section: Resultsmentioning

confidence: 99%

“…For example our work could be used in conjunction with Motion Layers [Lee and Shin 2001], Motion Warping [Witkin and Popovic 1995], Full-Body Inverse Kinematics [Yamane and Nakamura 2003], and Relationship Descriptors [Al-Asqhar et al 2013]. Because our approach is in real-time it allows for a tight feedback loop between these motion tools and animator edits.…”

Section: Applicationsmentioning

confidence: 99%

Learning an inverse rig mapping for character animation

Holden

Saito

Komura

2015

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

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Figure 1: Results of our method: animation is generated in the rig space for several different character rigs including a quadruped character, a deformable mesh character, a biped character, and a facial rig. This animation is generated via some external process, yet because it is mapped to the rig space, remains editable by animators. Dog rig and animation ( AbstractWe propose a general, real-time solution to the inversion of the rig function -the function which maps animation data from a character's rig to its skeleton. Animators design character movements in the space of an animation rig, and a lack of a general solution for mapping motions from the skeleton space to the rig space keeps the animators away from the state-of-the-art character animation methods, such as those seen in motion editing and synthesis. Our solution is to use non-linear regression on sparse example animation sequences constructed by the animators, to learn such a mapping offline. When new example motions are provided in the skeleton space, the learned mapping is used to estimate the rig space values that reproduce such a motion. In order to further improve the precision, we also learn the derivative of the mapping, such that the movements can be fine-tuned to exactly follow the given motion. We test and present our system through examples including full-body character models, facial models and deformable surfaces. With our system, animators have the freedom to attach any motion synthesis algorithms to an arbitrary rigging and animation pipeline, for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.

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“…et al [2010], which is based on a new structure called Interaction Mesh. This new structure is particularly efficiently to represent implicit spatial relationships between body parts, and has also been extended to account for relationships between human motions and its environment [Al-Asqhar et al 2013;Ho and Shum 2013].…”

Section: Related Workmentioning

confidence: 99%

Normalized Euclidean distance matrices for human motion retargeting

Bernardin

Hoyet

Mucherino

et al. 2017

Proceedings of the Tenth International Conference on Motion in Games

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Figure 1: (a) Example pose where the actor's hands come to a close distance. The same pose retargeted on a skeleton with longer forearms by (b) simply transferring joint angles or (c) using our normalized Euclidean distance matrix approach. ABSTRACTIn character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphologyindependent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Relationship descriptors for interactive motion adaptation

Cited by 57 publications

References 37 publications

A multi-resolution approach for adapting close character interaction

A multi-resolution approach for adapting close character interaction

Learning an inverse rig mapping for character animation

Normalized Euclidean distance matrices for human motion retargeting

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