Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Performance, Processing and Perception of Communicative Motion for Avatars and Agents
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-7801-7617
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. , 73 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 24
National Category
Computer and Information Science
Research subject
Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-218272ISBN: 978-91-7729-608-9 (print)OAI: oai:DiVA.org:kth-218272DiVA: diva2:1160154
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: 2017-11-27Bibliographically approved
List of papers
1. Towards Fully Automated Motion Capture of Signs -- Development and Evaluation of a Key Word Signing Avatar
Open this publication in new window or tab >>Towards Fully Automated Motion Capture of Signs -- Development and Evaluation of a Key Word Signing Avatar
2015 (English)In: ACM Transactions on Accessible Computing, ISSN 1936-7228, Vol. 7, no 2, 7:1-7:17 p.Article in journal (Refereed) Published
Abstract [en]

Motion capture of signs provides unique challenges in the field of multimodal data collection. The dense packaging of visual information requires high fidelity and high bandwidth of the captured data. Even though marker-based optical motion capture provides many desirable features such as high accuracy, global fitting, and the ability to record body and face simultaneously, it is not widely used to record finger motion, especially not for articulated and syntactic motion such as signs. Instead, most signing avatar projects use costly instrumented gloves, which require long calibration procedures. In this article, we evaluate the data quality obtained from optical motion capture of isolated signs from Swedish sign language with a large number of low-cost cameras. We also present a novel dual-sensor approach to combine the data with low-cost, five-sensor instrumented gloves to provide a recording method with low manual postprocessing. Finally, we evaluate the collected data and the dual-sensor approach as transferred to a highly stylized avatar. The application of the avatar is a game-based environment for training Key Word Signing (KWS) as augmented and alternative communication (AAC), intended for children with communication disabilities.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2015
Keyword
Augmentative and alternative communication (AAC), Motion capture, Sign language, Virtual characters
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:kth:diva-180427 (URN)10.1145/2764918 (DOI)000360070800004 ()2-s2.0-84935145760 (Scopus ID)
Note

 QC 2016-01-13

Available from: 2016-01-13 Created: 2016-01-13 Last updated: 2017-11-27Bibliographically approved
2. Real-time labeling of non-rigid motion capture marker sets
Open this publication in new window or tab >>Real-time labeling of non-rigid motion capture marker sets
2017 (English)In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 69, no Supplement C, 59-67 p.Article in journal (Refereed) Published
Abstract [en]

Passive optical motion capture is one of the predominant technologies for capturing high fidelity human motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to the fingers and face provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to non-rigid structures. The method is especially suited for large capture volumes and sparse marker sets. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). In three experiments, we evaluate the method for labeling a variety of marker configurations for finger and facial capture. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications.

Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Animation, Motion capture, Hand capture, Labeling
National Category
Media Engineering
Identifiers
urn:nbn:se:kth:diva-218254 (URN)10.1016/j.cag.2017.10.001 (DOI)2-s2.0-85032454324 (Scopus ID)
Note

QC 20171127

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-27Bibliographically approved
3. Automatic annotation of gestural units in spontaneous face-to-face interaction
Open this publication in new window or tab >>Automatic annotation of gestural units in spontaneous face-to-face interaction
2016 (English)In: MA3HMI 2016 - Proceedings of the Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, 2016, 15-19 p.Conference paper, Published paper (Refereed)
Abstract [en]

Speech and gesture co-occur in spontaneous dialogue in a highly complex fashion. There is a large variability in the motion that people exhibit during a dialogue, and different kinds of motion occur during different states of the interaction. A wide range of multimodal interface applications, for example in the fields of virtual agents or social robots, can be envisioned where it is important to be able to automatically identify gestures that carry information and discriminate them from other types of motion. While it is easy for a human to distinguish and segment manual gestures from a flow of multimodal information, the same task is not trivial to perform for a machine. In this paper we present a method to automatically segment and label gestural units from a stream of 3D motion capture data. The gestural flow is modeled with a 2-level Hierarchical Hidden Markov Model (HHMM) where the sub-states correspond to gesture phases. The model is trained based on labels of complete gesture units and self-adaptive manipulators. The model is tested and validated on two datasets differing in genre and in method of capturing motion, and outperforms a state-of-the-art SVM classifier on a publicly available dataset.

Keyword
Gesture recognition, Motion capture, Spontaneous dialogue, Hidden Markov models, Man machine systems, Markov processes, Online systems, 3D motion capture, Automatic annotation, Face-to-face interaction, Hierarchical hidden markov models, Multi-modal information, Multi-modal interfaces, Classification (of information)
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-202135 (URN)10.1145/3011263.3011268 (DOI)2-s2.0-85003571594 (Scopus ID)9781450345620 (ISBN)
Conference
2016 Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2016, 12 November 2016 through 16 November 2016
Funder
Swedish Research Council, 2010-4646
Note

Funding text: The work reported here is carried out within the projects: "Timing of intonation and gestures in spoken communication," (P12-0634:1) funded by the Bank of Sweden Tercentenary Foundation, and "Large-scale massively multimodal modelling of non-verbal behaviour in spontaneous dialogue," (VR 2010-4646) funded by Swedish Research Council.

Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2017-11-24Bibliographically approved
4. Animated Lombard speech: Motion capture, facial animation and visual intelligibility of speech produced in adverse conditions
Open this publication in new window or tab >>Animated Lombard speech: Motion capture, facial animation and visual intelligibility of speech produced in adverse conditions
2014 (English)In: Computer speech & language (Print), ISSN 0885-2308, E-ISSN 1095-8363, Vol. 28, no 2, 607-618 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we study the production and perception of speech in diverse conditions for the purposes of accurate, flexible and highly intelligible talking face animation. We recorded audio, video and facial motion capture data of a talker uttering a,set of 180 short sentences, under three conditions: normal speech (in quiet), Lombard speech (in noise), and whispering. We then produced an animated 3D avatar with similar shape and appearance as the original talker and used an error minimization procedure to drive the animated version of the talker in a way that matched the original performance as closely as possible. In a perceptual intelligibility study with degraded audio we then compared the animated talker against the real talker and the audio alone, in terms of audio-visual word recognition rate across the three different production conditions. We found that the visual intelligibility of the animated talker was on par with the real talker for the Lombard and whisper conditions. In addition we created two incongruent conditions where normal speech audio was paired with animated Lombard speech or whispering. When compared to the congruent normal speech condition, Lombard animation yields a significant increase in intelligibility, despite the AV-incongruence. In a separate evaluation, we gathered subjective opinions on the different animations, and found that some degree of incongruence was generally accepted.

Keyword
Lombard effect, Motion capture, Speech-reading, Lip-reading, Facial animation, Audio-visual intelligibility
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:kth:diva-141052 (URN)10.1016/j.csl.2013.02.005 (DOI)000329415400017 ()2-s2.0-84890567121 (Scopus ID)
Funder
Swedish Research Council, VR 2010-4646
Note

QC 20140212

Available from: 2014-02-12 Created: 2014-02-07 Last updated: 2017-12-06Bibliographically approved
5. Mimebot—Investigating the Expressibility of Non-Verbal Communication Across Agent Embodiments
Open this publication in new window or tab >>Mimebot—Investigating the Expressibility of Non-Verbal Communication Across Agent Embodiments
2017 (English)In: ACM Transactions on Applied Perception, ISSN 1544-3558, E-ISSN 1544-3965, Vol. 14, no 4, 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
Keyword
Motion capture, cross-mapping, perception
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-218270 (URN)10.1145/3127590 (DOI)000415407300003 ()2-s2.0-85029893975 (Scopus ID)
Note

QC 20171127

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-12-05Bibliographically approved

Open Access in DiVA

fulltext(5328 kB)33 downloads
File information
File name FULLTEXT01.pdfFile size 5328 kBChecksum SHA-512
21005ff4411aaf322b0bf698e7eecb69887ceb0c4d0f56e04feba7d8b289640d82a2113f88207d9c0625376586e51c1ec4686a2bcc6b3004f3426422cdb97403
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Alexanderson, Simon
By organisation
Speech, Music and Hearing, TMH
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 33 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 302 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf