skip to main content
research-article

Deep motifs and motion signatures

Published: 04 December 2018 Publication History

Abstract

Many analysis tasks for human motion rely on high-level similarity between sequences of motions, that are not an exact matches in joint angles, timing, or ordering of actions. Even the same movements performed by the same person can vary in duration and speed. Similar motions are characterized by similar sets of actions that appear frequently. In this paper we introduce motion motifs and motion signatures that are a succinct but descriptive representation of motion sequences. We first break the motion sequences to short-term movements called motion words, and then cluster the words in a high-dimensional feature space to find motifs. Hence, motifs are words that are both common and descriptive, and their distribution represents the motion sequence. To cluster words and find motifs, the challenge is to define an effective feature space, where the distances among motion words are semantically meaningful, and where variations in speed and duration are handled. To this end, we use a deep neural network to embed the motion words into feature space using a triplet loss function. To define a signature, we choose a finite set of motion-motifs, creating a bag-of-motifs representation for the sequence. Motion signatures are agnostic to movement order, speed or duration variations, and can distinguish fine-grained differences between motions of the same class. We illustrate examples of characterizing motion sequences by motifs, and for the use of motion signatures in a number of applications.

Supplementary Material

ZIP File (a187-aristidou.zip)
Supplemental files.
MP4 File (a187-aristidou.mp4)

References

[1]
Okan Arikan, David A. Forsyth, and James F. O'Brien. 2003. Motion Synthesis from Annotations. ACM Trans. Graph. 22, 3 (July 2003), 402--408.
[2]
Andreas Aristidou, Panayiotis Charalambous, and Yiorgos Chrysanthou. 2015. Emotion Analysis and Classification: Understanding the Performers' Emotions Using the LMA Entities. Comput. Graph. Forum 34, 6 (Sept. 2015), 262--276.
[3]
Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, and Ariel Shamir. 2018. Self-similarity Analysis for Motion Capture Cleaning. Comput. Graph. Forum 37, 2 (May 2018), 297--309.
[4]
Andreas Aristidou, Qiong Zeng, Efstathios Stavrakis, Kangkang Yin, Daniel Cohen-Or, Yiorgos Chrysanthou, and Baoquan Chen. 2017. Emotion Control of Unstructured Dance Movements. In Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA '17). ACM, New York, NY, USA, 9:1--9:10.
[5]
Andreas Baak, Meinard Müller, and Hans-Peter Seidel. 2008. An Efficient Algorithm for Keyframe-based Motion Retrieval in the Presence of Temporal Deformations. In Proceedings of the ACM International Conference on Multimedia Information Retrieval (MIR '08). ACM, New York, NY, USA, 451--458.
[6]
Jernej Barbič, Alla Safonova, Jia-Yu Pan, Christos Faloutsos, Jessica K. Hodgins, and Nancy S. Pollard. 2004. Segmenting Motion Capture Data into Distinct Behaviors. In Proceedings of Graphics Interface 2004 (GI '04). Canadian Human-Computer Communications Society, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, 185--194.
[7]
Philippe Beaudoin, Stelian Coros, Michiel van de Panne, and Pierre Poulin. 2008. Motion-motif Graphs. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '08). Eurographics Association, 117--126.
[8]
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation Learning: A Review and New Perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (Aug. 2013), 1798--1828.
[9]
Jürgen Bernard, Eduard Dobermann, Anna Vögele, Björn Krüger, Jörn Kohlhammer, and Dieter Fellner. 2017. Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data. In Proceedings of Visualization and Data Analysis (VDA '17). Society for Imaging Science and Technology, 34--45.
[10]
Jürgen Bernard, Nils Wilhelm, Björn Krüger, Thorsten May, Tobias Schreck, and Jörn Kohlhammer. 2013. MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. IEEE Transactions on Visualization and Computer Graphics 19, 12 (Dec. 2013), 2257--2266.
[11]
Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
[12]
Durell Bouchard and Norman I. Badler. 2015. Segmenting Motion Capture Data Using a Qualitative Analysis. In Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games (MIG '15). 23--30.
[13]
Jinxiang Chai and Jessica K. Hodgins. 2005. Performance Animation from Low-dimensional Control Signals. ACM Trans. Graph. 24, 3 (July 2005), 686--696.
[14]
Min-Wen Chao, Chao-Hung Lin, Jackie Assa, and Tong-Yee Lee. 2012. Human Motion Retrieval from Hand-Drawn Sketch. IEEE Transactions on Visualization and Computer Graphics 18, 5 (May 2012), 729--740.
[15]
Songle Chen, Zhengxing Sun, and Yan Zhang. 2015. Scalable Organization of Collections of Motion Capture Data via Quantitative and Qualitative Analysis. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR '15). 411--418.
[16]
Myung G. Choi, Kyungyong Yang, Takeo Igarashi, Jun Mitani, and Jehee Lee. 2012. Retrieval and Visualization of Human Motion Data via Stick Figures. Comput. Graph. Forum 31, 7 (2012), 2057--2065.
[17]
Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005. Learning a Similarity Metric Discriminatively, with Application to Face Verification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05). 539--546.
[18]
CMU. 2018. Carnegie Mellon University MoCap Database: http://mocap.cs.cmu.edu/. (2018).
[19]
Bruce Croft, Donald Metzler, and Trevor Strohman. 2009. Search Engines: Information Retrieval in Practice (1st ed.). Addison-Wesley Publishing Company, USA.
[20]
Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, and Cèdric Bray. 2004. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV. 1--22.
[21]
Zhigang Deng, Qin Gu, and Qing Li. 2009. Perceptually Consistent Example-based Human Motion Retrieval. In Proceedings of the Symposium on Interactive 3D Graphics and Games (I3D '09). 191--198.
[22]
DMCD. 2018. Dance Motion Capture Database: http://dancedb.cs.ucy.ac.cy/. (2018).
[23]
Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. 2012. What Makes Paris Look Like Paris? ACM Trans. Graph. 31, 4 (July 2012), 101:1--101:9.
[24]
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, and Trevor Darrell. 2017. Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. IEEE Trans. Pattern Anal. Mach. Intell. 39, 4 (April 2017), 677--691.
[25]
Matthew Field, David Stirling, Zengxi Pan, Montserrat Ros, and Fazel Naghdy. 2015. Recognizing Human Motions Through Mixture Modeling of Inertial Data. Pattern Recogn. 48, 8 (Aug. 2015), 2394--2406.
[26]
Kevin Forbes and Eugene Fiume. 2005. An Efficient Search Algorithm for Motion Data Using Weighted PCA. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '05). 67--76.
[27]
Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned Neural Networks for Character Control. ACM Trans. Graph. 36, 4 (July 2017), 42:1--42:13.
[28]
Daniel Holden, Jun Saito, and Taku Komura. 2016. A Deep Learning Framework for Character Motion Synthesis and Editing. ACM Trans. Graph. 35, 4 (July 2016), 138:1--138:11.
[29]
Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce. 2015. Learning Motion Manifolds with Convolutional Autoencoders. In SIGGRAPH Asia 2015 Technical Briefs (SA '15). 18:1--18:4.
[30]
Bing Hu, Yanping Chen, Jesin Zakaria, Liudmila Ulanova, and Eamonn Keogh. 2013. Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record. In IEEE 13th International Conference on Data Mining (ICDM'16). 281--290.
[31]
Yueqi Hu, Shuangyuan Wu, Shihong Xia, Jinghua Fu, and Wei Chen. 2010. Motion track: Visualizing variations of human motion data. In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis). 153--160.
[32]
Mohamed E. Hussein, Marwan Torki, Mohammad A. Gowayyed, and Motaz El-Saban. 2013. Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI '13). AAAI Press, 2466--2472.
[33]
Mubbasir Kapadia, I-kao Chiang, Tiju Thomas, Norman I. Badler, and Joseph T. Kider, Jr. 2013. Efficient Motion Retrieval in Large Motion Databases. In Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D '13). 19--28.
[34]
Ioannis Kapsouras and Nikos Nikolaidis. 2014. Action Recognition on Motion Capture Data Using a Dynemes and Forward Differences Representation. J. Vis. Comun. Image Represent. 25, 6 (Aug. 2014), 1432--1445.
[35]
Eamonn Keogh, Themistoklis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, and Marc Cardle. 2004. Indexing Large Human-motion Databases. In Proceedings of the International Conference on Very Large Data Bases (VLDB '04). 780--791.
[36]
Lucas Kovar and Michael Gleicher. 2004. Automated Extraction and Parameterization of Motions in Large Data Sets. ACM Trans. Graph. 23, 3 (Aug. 2004), 559--568.
[37]
Lucas Kovar, Michael Gleicher, and Frédéric Pighin. 2002. Motion Graphs. ACM Trans. Graph. 21, 3 (July 2002), 473--482.
[38]
Björn Krüger, Jochen Tautges, Andreas Weber, and Arno Zinke. 2010. Fast Local and Global Similarity Searches in Large Motion Capture Databases. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '10). Eurographics Association, 1--10.
[39]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (5 2015), 436--444.
[40]
Jehee Lee, Jinxiang Chai, Paul S. A. Reitsma, Jessica K. Hodgins, and Nancy S. Pollard. 2002. Interactive Control of Avatars Animated with Human Motion Data. ACM Trans. Graph. 21, 3 (July 2002), 491--500.
[41]
Fei-Fei Li and Pietro Perona. 2005. A Bayesian Hierarchical Model for Learning Natural Scene Categories. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05). 524--531.
[42]
Xiaosheng Li and Jessica Lin. 2017. Linear Time Complexity Time Series Classification with Bag-of-Pattern-Features. In IEEE International Conference on Data Mining (ICDM '17). 277--286.
[43]
Jessica Lin, Rohan Khade, and Yuan Li. 2012. Rotation-invariant Similarity in Time Series Using Bag-of-patterns Representation. J. Intell. Inf. Syst. 39, 2 (Oct. 2012), 287--315.
[44]
Feng Liu, Yueting Zhuang, Fei Wu, and Yunhe Pan. 2003. 3D Motion Retrieval with Motion Index Tree. Comput. Vis. Image Underst. 92, 2--3 (Nov. 2003), 265--284.
[45]
Guodong Liu, Jingdan Zhang, Wei Wang, and Leonard McMillan. 2005. A System for Analyzing and Indexing Human-motion Databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '05). 924--926.
[46]
Libin Liu and Jessica Hodgins. 2017. Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning. ACM Trans. Graph. 36, 3 (June 2017), 29:1--29:14.
[47]
Xin Liu, Gao-Feng He, Shu-Juan Peng, Yiu-ming Cheung, and Yuan Yan Tang. 2017. Efficient Human Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative Neighborhood Preserving Dictionary Learning. IEEE Transactions on Human-Machine Systems 47, 6 (Dec 2017), 763--776.
[48]
Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko, Helge Rhodin, Mohammad Shafiei, Hans-Peter Seidel, Weipeng Xu, Dan Casas, and Christian Theobalt. 2017. VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera. ACM Trans. Graph. 36, 4 (July 2017), 44:1--44:14.
[49]
Meinard Müller, Andreas Baak, and Hans-Peter Seidel. 2009. Efficient and Robust Annotation of Motion Capture Data. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '09). 17--26.
[50]
Meinard Müller and Tido Röder. 2006. Motion Templates for Automatic Classification and Retrieval of Motion Capture Data. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '06). Eurographics Association, 137--146.
[51]
Meinard Müller, Tido Röder, and Michael Clausen. 2005. Efficient Content-based Retrieval of Motion Capture Data. ACM Trans. Graph. 24, 3 (July 2005), 677--685.
[52]
Georgios Pavlakos, Xiaowei Zhou, Konstantinos G. Derpanis, and Kostas Daniilidis. 2017. Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '17). 1--10.
[53]
Xue Bin Peng, Glen Berseth, Kangkang Yin, and Michiel Van De Panne. 2017. DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning. ACM Trans. Graph. 36, 4 (July 2017), 41:1--41:13.
[54]
Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2012. Searching and Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '12). ACM, New York, NY, USA, 262--270.
[55]
Michalis Raptis, Darko Kirovski, and Hugues Hoppe. 2011. Real-time Classification of Dance Gestures from Skeleton Animation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '11). 147--156.
[56]
Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas. 2000. The Earth Mover's Distance As a Metric for Image Retrieval. Int. J. Comput. Vision 40, 2 (Nov. 2000), 99--121.
[57]
Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '15). 815--823.
[58]
Saurabh Singh, Abhinav Gupta, and Alexei A. Efros. 2012. Unsupervised Discovery of Mid-Level Discriminative Patches. Springer Berlin Heidelberg, Berlin, Heidelberg, 73--86.
[59]
Chuan Sun, Imran N. Junejo, and Hassan Foroosh. 2011. Motion Retrieval Using Low-Rank Subspace Decomposition of Motion Volume. Comput. Graph. Forum 30, 7 (November 2011), 1953--1962.
[60]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '15). 1--9.
[61]
Wataru Takano and Yoshihiko Nakamura. 2015. Symbolically Structured Database for Human Whole Body Motions Based on Association Between Motion Symbols and Motion Words. Robot. Auton. Syst. 66, C (April 2015), 75--85.
[62]
Jochen Tautges, Arno Zinke, Björn Krüger, Jan Baumann, Andreas Weber, Thomas Helten, Meinard Müller, Hans-Peter Seidel, and Bernd Eberhardt. 2011. Motion Reconstruction Using Sparse Accelerometer Data. ACM Trans. Graph. 30, 3 (May 2011), 18:1--18:12.
[63]
Matthew Thorne, David Burke, and Michiel van de Panne. 2004. Motion Doodles: An Interface for Sketching Character Motion. ACM Trans. Graph. 23, 3 (Aug. 2004), 424--431.
[64]
M. Alex O. Vasilescu. 2002. Human motion signatures: analysis, synthesis, recognition. In Proceedings of the International Conference on Pattern Recognition, Vol. 3. IEEE, 456--460.
[65]
Raviteja Vemulapalli, Felipe Arrate, and Rama Chellappa. 2014. Human Action Recognition by Representing 3D Skeletons As Points in a Lie Group. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14). 588--595.
[66]
Anna Vögele, Björn Krüger, and Reinhard Klein. 2014. Efficient Unsupervised Temporal Segmentation of Human Motion. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '14). Eurographics Association, 167--176.
[67]
Jing Wang and Bobby Bodenheimer. 2003. An Evaluation of a Cost Metric for Selecting Transitions Between Motion Segments. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '03). Eurographics Association, 232--238.
[68]
Yingying Wang and Michael Neff. 2015. Deep Signatures for Indexing and Retrieval in Large Motion Databases. In Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games (MIG '15). ACM, New York, NY, USA, 37--45.
[69]
Ian H. Witten, Timothy C. Bell, and Alistair Moffat. 1994. Managing Gigabytes: Compressing and Indexing Documents and Images (1st ed.). John Wiley & Sons, Inc.
[70]
Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, and Philipp Krähenbühl. 2017. Sampling Matters in Deep Embedding Learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV '17). 2859--2867.
[71]
Shuangyuan Wu, Zhaoqi Wang, and Shihong Xia. 2009. Indexing and Retrieval of Human Motion Data by a Hierarchical Tree. In Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology (VRST '09). 207--214.
[72]
Jun Xiao, Yinfu Feng, Mingming Ji, Xiaosong Yang, Jian J. Zhang, and Yueting Zhuang. 2015. Sparse Motion Bases Selection for Human Motion Denoising. Signal Process. 110, C (May 2015), 108--122.
[73]
Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamon Keogh. 2017. Matrix Profile VI: Meaningful Multidimensional Motif Discovery. In IEEE 17th International Conference on Data Mining (ICDM'17). 1317--1322.
[74]
Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2018. Time Series Joins, Motifs, Discords and Shapelets: A Unifying View That Exploits the Matrix Profile. Data Min. Knowl. Discov. 32, 1 (Jan. 2018), 83--123.
[75]
Byoung-Kee Yi, H. V. Jagadish, and Christos Faloutsos. 1998. Efficient Retrieval of Similar Time Sequences Under Time Warping. In Proceedings of the Fourteenth International Conference on Data Engineering (ICDE '98). IEEE Computer Society, Washington, DC, USA, 201--208.
[76]
Sergey Zagoruyko and Nikos Komodakis. 2015. Learning to compare image patches via convolutional neural networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '15). 4353--4361.
[77]
Feng Zhou, Fernando De la Torre, and Jessica K. Hodgins. 2013. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion. IEEE Trans. Pattern Anal. Mach. Intell. 35, 3 (March 2013), 582--596.

Cited By

View all
  • (2024)AI-based Real-time Online Random-play Dance PlatformJournal of Digital Contents Society10.9728/dcs.2024.25.3.68525:3(685-693)Online publication date: 31-Mar-2024
  • (2024)Modifying Gesture Style with Impression WordsProceedings of the 24th ACM International Conference on Intelligent Virtual Agents10.1145/3652988.3673931(1-9)Online publication date: 16-Sep-2024
  • (2024)WalkTheDog: Cross-Morphology Motion Alignment via Phase ManifoldsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657508(1-10)Online publication date: 13-Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 37, Issue 6
December 2018
1401 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/3272127
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2018
Published in TOG Volume 37, Issue 6

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. animation
  2. convolutional network
  3. motif
  4. motion signature
  5. motion word
  6. triplet loss

Qualifiers

  • Research-article

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)76
  • Downloads (Last 6 weeks)7
Reflects downloads up to 30 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)AI-based Real-time Online Random-play Dance PlatformJournal of Digital Contents Society10.9728/dcs.2024.25.3.68525:3(685-693)Online publication date: 31-Mar-2024
  • (2024)Modifying Gesture Style with Impression WordsProceedings of the 24th ACM International Conference on Intelligent Virtual Agents10.1145/3652988.3673931(1-9)Online publication date: 16-Sep-2024
  • (2024)WalkTheDog: Cross-Morphology Motion Alignment via Phase ManifoldsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657508(1-10)Online publication date: 13-Jul-2024
  • (2024)Iterative Motion Editing with Natural LanguageACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657447(1-9)Online publication date: 13-Jul-2024
  • (2024)PoseCoach: A Customizable Analysis and Visualization System for Video-Based Running CoachingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.323085530:7(3180-3195)Online publication date: 1-Jul-2024
  • (2024)Spatially-Adaptive Instance Normalization for Generation of More Style-Recognizable Motions2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)10.1109/IAEAC59436.2024.10504037(1568-1572)Online publication date: 15-Mar-2024
  • (2024)MoMask: Generative Masked Modeling of 3D Human Motions2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00186(1900-1910)Online publication date: 16-Jun-2024
  • (2024)Crowd-sourced Evaluation of Combat Animations2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR59861.2024.00015(60-65)Online publication date: 17-Jan-2024
  • (2024)Bridging the Gap Between Human Motion and Action Semantics via Kinematic PhrasesComputer Vision – ECCV 202410.1007/978-3-031-73242-3_13(223-240)Online publication date: 29-Sep-2024
  • (2023)SAME: Skeleton-Agnostic Motion Embedding for Character AnimationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618206(1-11)Online publication date: 10-Dec-2023
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media