Controller Design for Multi‐Skilled Bipedal Characters. (23rd May 2015)

• Record Type: Journal Article
• Authors: Firmin, M.; van de Panne, M.
• Is Part Of: Computer graphics forum. Volume 34:Number 8(2015)
• Page Start: 50
• Page End: 63
• Publication Date: 2015-05-23
• Subjects: physically based animation -- human simulation -- motion control -- I.3.3 [Computer Graphics]: Animation—Physical Simulation; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.12607
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract : Developing motions for simulated humanoids remains a challenging problem. While there exists a multitude of approaches, few of these are reimplemented or reused by others. The predominant focus of papers in the area remains on algorithmic novelty, due to the difficulty and lack of incentive to more fully explore what can be accomplished within the scope of existing methodologies. We develop a language, based on common features found across physics‐based character animation research, that facilitates the controller authoring process. By specifying motion primitives over a number of phases, our language has been used to design over 25 controllers for motions ranging from simple static balanced poses, to highly dynamic stunts. Abstract: Developing motions for simulated humanoids remains a challenging problem. While there exists a multitude of approaches, few of these are reimplemented or reused by others. The predominant focus of papers in the area remains on algorithmic novelty, due to the difficulty and lack of incentive to more fully explore what can be accomplished within the scope of existing methodologies. We develop a language, based on common features found across physics‐based character animation research, that facilitates the controller authoring process. By specifying motion primitives over a number of phases, our language has been used to design over 25 controllers for motions ranging from simple static balanced poses, to highly dynamic stunts. Controller sequencing is supported in two ways. Naive integration of controllers is achieved by using highly stable pose controllers (such as a standing or squatting) as intermediate transitions. More complex controller connections are automatically learned through an optimization process. The robustness of our system is demonstrated via random walkthroughs of our integrated set of controllers.