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MoGlow: Probabilistic and controllable motion synthesis using normalising flows
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-1643-1054
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-7801-7617
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-1399-6604
2020 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 39, no 6, p. 1-14, article id 236Article in journal (Refereed) Published
Abstract [en]

Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics. This paper introduces a new class of probabilistic, generative, and controllable motion-data models based on normalising flows. Models of this kind can describe highly complex distributions, yet can be trained efficiently using exact maximum likelihood, unlike GANs or VAEs. Our proposed model is autoregressive and uses LSTMs to enable arbitrarily long time-dependencies. Importantly, is is also causal, meaning that each pose in the output sequence is generated without access to poses or control inputs from future time steps; this absence of algorithmic latency is important for interactive applications with real-time motion control. The approach can in principle be applied to any type of motion since it does not make restrictive, task-specific assumptions regarding the motion or the character morphology. We evaluate the models on motion-capture datasets of human and quadruped locomotion. Objective and subjective results show that randomly-sampled motion from the proposed method outperforms task-agnostic baselines and attains a motion quality close to recorded motion capture.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2020. Vol. 39, no 6, p. 1-14, article id 236
Keywords [en]
Generative models, machine learning, normalising flows, Glow, footstep analysis, data dropout
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-282300DOI: 10.1145/3414685.3417836ISI: 000595589100076Scopus ID: 2-s2.0-85096681707OAI: oai:DiVA.org:kth-282300DiVA, id: diva2:1471598
Conference
The 13th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia, Online event, December 4–13, 2020
Projects
VR proj. 2018-05409 (StyleBot)SSF no. RIT15-0107 (EACare)Wallenberg AI, Autonomous Systems and Software Program (WASP)
Funder
Swedish Research Council, 2018-05409Swedish Foundation for Strategic Research, RIT15-0107Knut and Alice Wallenberg Foundation, WASP
Note

QC 20200929

Available from: 2020-09-29 Created: 2020-09-29 Last updated: 2024-02-19Bibliographically approved

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fulltext(1053 kB)275 downloads
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Authority records

Henter, Gustav EjeAlexanderson, SimonBeskow, Jonas

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CiteExportLink to record
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