Optimization-based motion retargeting integrating spatial and dynamic constraints for humanoid

Lang

Proceedings of the 10th International Conference on Computer Graphics Theory and Applications

et al. 2015

Character animation has been used in many fields, such as film making, virtual reality interaction and video games. Video game needs a large set of character animations for users to trigger. However, it is a complex and tedious work for animators to manufacture all the transition animations between each pair of character animations. In this paper, a novel rapid method is presented to generate transition animation automatically. Firstly, animations in the game animation set are classified. Secondly, the time length during motion interpolation is calculated automatically, which is decided by the angle velocity of character's joints. The method is called variable timing method. Lastly, by using hierarchical interpolation method, postures interpolate between different animations. The transition animation can be quickly acquired, according with human dynamics. In this way, we can get a natural movement by connecting animations together. Experimental results demonstrate that our method can be effectively and efficiently applied to generate transition animations between various character animations.

Section: Related Workmentioning

confidence: 99%

Automatic Variable-timing Animation Transition based on Hierarchical Interpolation Method

Lang

Proceedings of the 10th International Conference on Computer Graphics Theory and Applications

et al. 2015

“…5. The effect of approximating the nonlinear dynamic equation of torque to be linear, (7), can be seen more clearly when the limit is set to be stricter, for example, set to half the real limit as shown Red, blue, and black lines represent original data, generated data, and limit lines, respectively. X-axis is time and Y-axis is each value specified in each subfigure.…”

Section: Filteringmentioning

confidence: 99%

“…They seemed to focus more on objective functions. Moulard et al 7 relied also on box limits and further took ZMP into account. Perhaps the most advanced work that is close to the offline optimization part of our work is Khoury et al 8 They considered many physical limits similar to ref.…”

Section: Introductionmentioning

confidence: 99%

Humanoid robot upper body motion generation using B-spline-based functions

Ruchanurucks

2014

Robotica

SUMMARYThis paper aims to represent a human's upper body motion using a humanoid robot. As a robot has different physical limitations than a human, we present a method that can filter the trajectories to meet the limitations. The filtering can be used directly and also can be used as a constraint for optimization. It will be shown to be applicable both offline and online. Many physical attributes, namely angle, collision, velocity, and dynamic torque, are represented as B-spline explicit functions. The B-spline coefficients are then calculated to limit the physical attributes. This explicit method can guarantee that many limits are met. Hence, it is better than many methods that only reduce such physical attributes using objective functions.

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

“…Deriving motions from a pre-segmented motion capture database [7] [11] also have been actively developed. To make robot motions look as much like original human motions as possible, optimization-based methods [12] [13] [14] have been developed. However their cost functions in optimization are fixed regardless of target tasks; There is no guarantee that those functions are essential for any other motions.…”

Section: Introductionmentioning

confidence: 99%

Extraction of person-specific motion style based on a task model and imitation by humanoid robot

Okamoto

Shiratori

Glisson

et al. 2014

In this paper, we present a humanoid robot which extracts and imitates the person-specific differences in motions, which we will call style. Synthesizing human-like and stylistic motion variations according to specific scenarios is becoming important for entertainment robots, and imitation of styles is one variation which makes robots more amiable. Our approach extends a learning from observation (LFO) paradigm which enables robots to understand what a human is doing and to extract reusable essences to be learned. The focus is on styles in the domain of LFO and the representation of them using the reusable essences. In this paper, we design an abstract model of a target motion defined in LFO, observe human demonstrations through the model, and formulate the representation of styles in the context of LFO. Then we introduce a framework of generating robot motions that reflect styles which are automatically extracted from human demonstrations. To verify our proposed method we applied it to a ring toss game, and generated robot motions for a physical humanoid robot. Styles from each of three random players were extracted automatically from their demonstrations, and used for generating robot motions. The robot imitates the styles of each player without exceeding the limitation of its physical constraints, while tossing the rings to the goal.