Applying Evolutionary Computation Operators for Automatic Human Motion Generation in Computer Animation and Video Games

2018 ◽  
pp. 247-258
Author(s):  
Luis de la Vega-Hazas ◽  
Francisco Calatayud ◽  
Andrés Iglesias
Keyword(s):  
Video Games ◽  
Evolutionary Computation ◽  
Computer Animation ◽  
Human Motion ◽  
Motion Generation

Parameterization of Three-Dimensional Rigid Body Guidance Incorporating Planar Gear Connections in a Spatial Closed-Loop Mechanism With Parallel Consecutive Axes

2008 ◽  
Author(s):  
Javier Rolda´n Mckinley ◽  
Carl Crane ◽  
David B. Dooner
Keyword(s):  
Computer Animation ◽  
Closed Loop ◽  
Three Dimensional ◽  
Motion Generation ◽  
Repetitive Motion ◽  
End Effector ◽  
Spatial Mechanism ◽  
Joint Axis ◽  
Position And Orientation ◽  
Six Degree Of Freedom

This paper introduces a reconfigurable closed-loop spatial mechanism that can be applied to repetitive motion tasks. The concept is to incorporate five pairs of non-circular gears into a six degree-of–freedom closed-loop spatial chain. The gear pairs are designed based on given mechanism parameters and a user defined motion specification of a coupler link of the mechanism. It is shown in the paper that planar gear pairs can be used if the spatial closed-loop chain is comprised of six pairs of parallel joint axes, i.e. the first joint axis is parallel to the second, the third is parallel to the fourth, ..., and the eleventh is parallel to the twelfth. This paper presents the synthesis of the gear pairs that satisfy a specified three-dimensional position and orientation need. Numerical approximations were used in the synthesis the non-circular gear pairs by introducing an auxiliary monotonic parameter associated to each end-effector position to parameterize the motion needs. The findings are supported by a computer animation. No previous known literature incorporates planar non-circular gears to fulfill spatial motion generation needs.

Sensorimotor Control for Human Motion Generation—A Review

2012 ◽  
Vol 1 (2) ◽  
pp. 161-179 ◽  
Author(s):  
Chen-Hui Hu ◽  
Qin-Huai Zhang ◽  
Wen-Chieh Lin
Keyword(s):  
Sensorimotor Control ◽  
Human Motion ◽  
Motion Generation

A Robot Arm Motion Generation Based on Learning of Objects Shape and Human Motion

2018 ◽  
Vol 2018 (0) ◽  
pp. 2A1-F14
Author(s):  
Sho TAJIMA ◽  
Tokuo TSUJI ◽  
Yosuke SUZUKI ◽  
Tetsuyou WATANABE ◽  
Kenichi MOROOKA ◽  
...  
Keyword(s):  
Human Motion ◽  
Robot Arm ◽  
Motion Generation ◽  
Arm Motion

scholarly journals Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

2020 ◽  
Vol 34 (07) ◽  
pp. 12281-12288
Author(s):  
Zhenyi Wang ◽  
Ping Yu ◽  
Yang Zhao ◽  
Ruiyi Zhang ◽  
Yufan Zhou ◽  
...  
Keyword(s):  
Human Action ◽  
Human Motion ◽  
Motion Generation ◽  
Action Sequence ◽  
Sequence Generation ◽  
Latent Space ◽  
Action Sequences ◽  
Mixed Classes ◽  
Standard Action ◽  
Net Framework

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which can not only generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.

scholarly journals Crowd Simulation with Arrival Time Constraints

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1804
Author(s):  
Mankyu Sung ◽  
SeongKi Kim
Keyword(s):  
User Interface ◽  
Video Games ◽  
Path Planning ◽  
Computer Animation ◽  
City Planning ◽  
Crowd Simulation ◽  
Time Constraint ◽  
Order Constraints ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Finding collision-free paths for crowd simulation has been a core technique in video games and the film industry; it has drawn a great deal of attention from computer animation researchers for several decades. Additionally, theoretical modeling of pedestrian has been a hot topic in physics as well because it allows us to predict any architectural failure of buildings and many city planning problems. However, the existing studies for path planning cannot guarantee the arrival order, which is critical in many cases, such as arrival symmetry of the characters within video games or films. To resolve this issue, a path planning algorithm has been developed with a novel method for satisfying the arrival-order constraints. The time constraint we suggest is the temporal duration for each character, specifying the order in which they arrive at their target positions. In addition to the algorithm that guarantees the arrival order of objects, a new user interface is suggested for setting up the arrival order. Through several experiments, the proposed algorithm was verified, and can successfully find collision-free paths, while satisfying the time constraint set by the new user interface. Given the available literature, the suggested algorithm and the interface are the first that support arrival order, and their usability is proven by user studies.

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