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Real-time labeling of non-rigid motion capture marker sets
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: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 69, no Supplement C, p. 59-67Article 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. Vol. 69, no Supplement C, p. 59-67
Keyword [en]
Animation, Motion capture, Hand capture, Labeling
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:kth:diva-218254DOI: 10.1016/j.cag.2017.10.001ISI: 000418980500008Scopus ID: 2-s2.0-85032454324OAI: oai:DiVA.org:kth-218254DiVA, id: diva2:1160124
Note

QC 20171127

Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-01-16Bibliographically 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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