Real-time performance-driven finger motion synthesis

Mousas, Christos; Anagnostopoulos, Christos‐Nikolaos

doi:10.1016/j.cag.2017.03.001

Cited by 20 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple example includes the relationship description [51], which would allow the developed framework to handle interactions between multiple characters more efficiently. Finally, the extension of the current HMM to a hierarchical model [52] with reactive interpolations [53] is another improvement that would benefit the MOP framework, since it would predict the progression of the character's motion while synthesizing the motion sequences, which is a functionality that is not provided in the current version of the MOP framework.…”

Section: Discussionmentioning

confidence: 99%

Master of Puppets: An Animation-by-Demonstration Computer Puppetry Authoring Framework

Cui¹,

Mousas²

2018

3D Res

Self Cite

View full text Add to dashboard Cite

This paper presents Master of Puppets (MOP), an animation-by-demonstration framework that allows users to control the motion of virtual characters (puppets) in real time. In the first step, the user is asked to perform the necessary actions that correspond to the character's motions. The user's actions are recorded, and a hidden Markov model (HMM) is used to learn the temporal profile of the actions. During the runtime of the framework, the user controls the motions of the virtual character based on the specified activities. The advantage of the MOP framework is that it recognizes and follows the progress of the user's actions in real time. Based on the forward algorithm, the method predicts the evolution of the user's actions, which corresponds to the evolution of the character's motion. This method treats characters as puppets that can perform only one motion at a time. This means that combinations of motion segments (motion synthesis), as well as the interpolation of individual motion sequences, are not provided as functionalities. By implementing the framework and presenting several computer puppetry scenarios, its efficiency and flexibility in animating virtual characters is demonstrated.

show abstract

Section: Discussionmentioning

confidence: 99%

Master of Puppets: An Animation-by-Demonstration Computer Puppetry Authoring Framework

Cui¹,

Mousas²

2018

3D Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pollard and Zordan [PZ05] developed a hand grasp motion controller to generate various types of hand grasping motions using physical simulation. Hand motion synthesis methods [JHS12, JH05, MA17] have been proposed that estimate finger gestures according to the body input motion. Complementary research has considered collision avoidance between hand and object during grasping.…”

Section: Related Workmentioning

confidence: 99%

Primitive Object Grasping for Finger Motion Synthesis

Hwang

Park

Suh³

et al. 2020

Computer Graphics Forum

View full text Add to dashboard Cite

We developed a new framework to generate hand and finger grasping motions. The proposed framework provides online adaptation to the position and orientation of objects and can generate grasping motions even when the object shape differs from that used during motion capture. This is achieved by using a mesh model, which we call primitive object grasping (POG), to represent the object grasping motion. The POG model uses a mesh deformation algorithm that keeps the original shape of the mesh while adapting to varying constraints. These characteristics are beneficial for finger grasping motion synthesis that satisfies constraints for mimicking the motion capture sequence and the grasping points reflecting the shape of the object. We verify the adaptability of the proposed motion synthesizer according to its position/orientation and shape variations of different objects by using motion capture sequences for grasping primitive objects, namely, a sphere, a cylinder, and a box. In addition, a different grasp strategy called a three‐finger grasp is synthesized to validate the generality of the POG‐based synthesis framework.

show abstract

“…HRLB^2 approaches high-dimensional state-action spaces by decomposing them into a set of smaller sub-problems using temporally extended actions [27]. This procedure has been widely used to tackle large problems that can be represented at different levels of abstractions [28][29][30]. Furthermore, this hierarchical decomposition allows incorporating expert knowledge into the model and, in similar RL configurations to ours, it also reduces the exploration process without sacrificing learning performance [31,32].…”

Section: Related Workmentioning

confidence: 99%

HRLB⌃2: A Reinforcement Learning Based Framework for Believable Bots

Cruz

Uresti

2018

Applied Sciences

View full text Add to dashboard Cite

The creation of believable behaviors for Non-Player Characters (NPCs) is key to improve the players’ experience while playing a game. To achieve this objective, we need to design NPCs that appear to be controlled by a human player. In this paper, we propose a hierarchical reinforcement learning framework for believable bots (HRLB⌃2). This novel approach has been designed so it can overcome two main challenges currently faced in the creation of human-like NPCs. The first difficulty is exploring domains with high-dimensional state–action spaces, while satisfying constraints imposed by traits that characterize human-like behavior. The second problem is generating behavior diversity, by also adapting to the opponent’s playing style. We evaluated the effectiveness of our framework in the domain of the 2D fighting game named Street Fighter IV. The results of our tests demonstrate that our bot behaves in a human-like manner.

show abstract

Real-time performance-driven finger motion synthesis

Cited by 20 publications

References 17 publications

Master of Puppets: An Animation-by-Demonstration Computer Puppetry Authoring Framework

Master of Puppets: An Animation-by-Demonstration Computer Puppetry Authoring Framework

Primitive Object Grasping for Finger Motion Synthesis

HRLB⌃2: A Reinforcement Learning Based Framework for Believable Bots

Contact Info

Product

Resources

About