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Mimebot—Investigating the Expressibility of Non-Verbal Communication Across Agent Embodiments
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-7801-7617
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
2017 (English)In: ACM Transactions on Applied Perception, ISSN 1544-3558, E-ISSN 1544-3965, Vol. 14, no 4, article id 24Article in journal (Refereed) Published
Abstract [en]

Unlike their human counterparts, artificial agents such as robots and game characters may be deployed with a large variety of face and body configurations. Some have articulated bodies but lack facial features, and others may be talking heads ending at the neck. Generally, they have many fewer degrees of freedom than humans through which they must express themselves, and there will inevitably be a filtering effect when mapping human motion onto the agent. In this article, we investigate filtering effects on three types of embodiments: (a) an agent with a body but no facial features, (b) an agent with a head only, and (c) an agent with a body and a face. We performed a full performance capture of a mime actor enacting short interactions varying the non-verbal expression along five dimensions (e.g., level of frustration and level of certainty) for each of the three embodiments. We performed a crowd-sourced evaluation experiment comparing the video of the actor to the video of an animated robot for the different embodiments and dimensions. Our findings suggest that the face is especially important to pinpoint emotional reactions but is also most volatile to filtering effects. The body motion, on the other hand, had more diverse interpretations but tended to preserve the interpretation after mapping and thus proved to be more resilient to filtering.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. Vol. 14, no 4, article id 24
Keyword [en]
Motion capture, cross-mapping, perception
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-218270DOI: 10.1145/3127590ISI: 000415407300003Scopus ID: 2-s2.0-85029893975OAI: oai:DiVA.org:kth-218270DiVA, id: diva2:1160136
Note

QC 20171127

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Performance, Processing and Perception of Communicative Motion for Avatars and Agents
Open this publication in new window or tab >>Performance, Processing and Perception of Communicative Motion for Avatars and Agents
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Artificial agents and avatars are designed with a large variety of face and body configurations. Some of these (such as virtual characters in films) may be highly realistic and human-like, while others (such as social robots) have considerably more limited expressive means. In both cases, human motion serves as the model and inspiration for the non-verbal behavior displayed. This thesis focuses on increasing the expressive capacities of artificial agents and avatars using two main strategies: 1) improving the automatic capturing of the most communicative areas for human communication, namely the face and the fingers, and 2) increasing communication clarity by proposing novel ways of eliciting clear and readable non-verbal behavior.

The first part of the thesis covers automatic methods for capturing and processing motion data. In paper A, we propose a novel dual sensor method for capturing hands and fingers using optical motion capture in combination with low-cost instrumented gloves. The approach circumvents the main problems with marker-based systems and glove-based systems, and it is demonstrated and evaluated on a key-word signing avatar. In paper B, we propose a robust method for automatic labeling of sparse, non-rigid motion capture marker sets, and we evaluate it on a variety of marker configurations for finger and facial capture. In paper C, we propose an automatic method for annotating hand gestures using Hierarchical Hidden Markov Models (HHMMs).

The second part of the thesis covers studies on creating and evaluating multimodal databases with clear and exaggerated motion. The main idea is that this type of motion is appropriate for agents under certain communicative situations (such as noisy environments) or for agents with reduced expressive degrees of freedom (such as humanoid robots). In paper D, we record motion capture data for a virtual talking head with variable articulation style (normal-to-over articulated). In paper E, we use techniques from mime acting to generate clear non-verbal expressions custom tailored for three agent embodiments (face-and-body, face-only and body-only).

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. p. 73
Series
TRITA-CSC-A, ISSN 1653-5723 ; 24
National Category
Computer and Information Sciences
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-218272 (URN)978-91-7729-608-9 (ISBN)
Public defence
2017-12-15, F3, Lindstedtsvägen 26, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20171127

Available from: 2017-11-27 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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