Workshop: Data-Driven Animation Technology (D2AT)

Hongchuan Yu; Taku Komura; Jian J. Zhang

doi:10.1145/3154457.3154458

Workshop: Data-Driven Animation Technology (D2AT)

Hongchuan Yu, Taku Komura, Jian J. Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

The aim of this full-day workshop in conjunction with SIGGRAPH Asia 2017 is to bring together researchers from diverse backgrounds, such as computer graphics, computer vision, virtual reality, human computer interactions and machine learning, with a common interest in data-driven, realistic animation. Interests in this emerging topic may stem from a variety of sources, e.g. point cloud data collected from laser scanners or RGBD cameras such as KINECT, high resolution geometry reconstructed by Structure from Motion (SfM), motion capture devices including high frame-rate optical trackers and IMUs, GPS data from millions of mobile devices etc. Despite the high dimensionality and the huge volume of the dataset, the boost in machine learning and big data technologies are allowing researchers to extract important features from them for which can be applied for data analysis, synthesis and editing.

Original language	English
Title of host publication	SIGGRAPH Asia 2017 Workshops
Publisher	Association for Computing Machinery, Inc
Number of pages	4
Volume	36
ISBN (Electronic)	9781450355780
DOIs	https://doi.org/10.1145/3154457.3154458
Publication status	Published - 27 Nov 2017
Event	SIGGRAPH Asia 2017 Workshops, SA 2017 - Bangkok, Thailand Duration: 27 Nov 2017 → 30 Nov 2017

Conference

Conference	SIGGRAPH Asia 2017 Workshops, SA 2017
Country/Territory	Thailand
City	Bangkok
Period	27/11/17 → 30/11/17

Keywords / Materials (for Non-textual outputs)

Data-driven
Emotion data
Machine learning
Motion data

Access to Document

10.1145/3154457.3154458Licence: All Rights Reserved

a1-yuAccepted author manuscript, 362 KB

https://dl.acm.org/citation.cfm?doid=3154457.3154458Licence: All Rights Reserved

Cite this

@inproceedings{d375aae0130945d2af54543f6c2d8609,

title = "Workshop: Data-Driven Animation Technology (D2AT)",

abstract = "The aim of this full-day workshop in conjunction with SIGGRAPH Asia 2017 is to bring together researchers from diverse backgrounds, such as computer graphics, computer vision, virtual reality, human computer interactions and machine learning, with a common interest in data-driven, realistic animation. Interests in this emerging topic may stem from a variety of sources, e.g. point cloud data collected from laser scanners or RGBD cameras such as KINECT, high resolution geometry reconstructed by Structure from Motion (SfM), motion capture devices including high frame-rate optical trackers and IMUs, GPS data from millions of mobile devices etc. Despite the high dimensionality and the huge volume of the dataset, the boost in machine learning and big data technologies are allowing researchers to extract important features from them for which can be applied for data analysis, synthesis and editing.",

keywords = "Data-driven, Emotion data, Machine learning, Motion data",

author = "Hongchuan Yu and Taku Komura and Zhang, {Jian J.}",

year = "2017",

month = nov,

day = "27",

doi = "10.1145/3154457.3154458",

language = "English",

volume = "36",

booktitle = "SIGGRAPH Asia 2017 Workshops",

publisher = "Association for Computing Machinery, Inc",

note = "SIGGRAPH Asia 2017 Workshops, SA 2017 ; Conference date: 27-11-2017 Through 30-11-2017",

}

TY - GEN

T1 - Workshop

T2 - SIGGRAPH Asia 2017 Workshops, SA 2017

AU - Yu, Hongchuan

AU - Komura, Taku

AU - Zhang, Jian J.

PY - 2017/11/27

Y1 - 2017/11/27

N2 - The aim of this full-day workshop in conjunction with SIGGRAPH Asia 2017 is to bring together researchers from diverse backgrounds, such as computer graphics, computer vision, virtual reality, human computer interactions and machine learning, with a common interest in data-driven, realistic animation. Interests in this emerging topic may stem from a variety of sources, e.g. point cloud data collected from laser scanners or RGBD cameras such as KINECT, high resolution geometry reconstructed by Structure from Motion (SfM), motion capture devices including high frame-rate optical trackers and IMUs, GPS data from millions of mobile devices etc. Despite the high dimensionality and the huge volume of the dataset, the boost in machine learning and big data technologies are allowing researchers to extract important features from them for which can be applied for data analysis, synthesis and editing.

AB - The aim of this full-day workshop in conjunction with SIGGRAPH Asia 2017 is to bring together researchers from diverse backgrounds, such as computer graphics, computer vision, virtual reality, human computer interactions and machine learning, with a common interest in data-driven, realistic animation. Interests in this emerging topic may stem from a variety of sources, e.g. point cloud data collected from laser scanners or RGBD cameras such as KINECT, high resolution geometry reconstructed by Structure from Motion (SfM), motion capture devices including high frame-rate optical trackers and IMUs, GPS data from millions of mobile devices etc. Despite the high dimensionality and the huge volume of the dataset, the boost in machine learning and big data technologies are allowing researchers to extract important features from them for which can be applied for data analysis, synthesis and editing.

KW - Data-driven

KW - Emotion data

KW - Machine learning

KW - Motion data

UR - http://www.scopus.com/inward/record.url?scp=85040226703&partnerID=8YFLogxK

U2 - 10.1145/3154457.3154458

DO - 10.1145/3154457.3154458

M3 - Conference contribution

AN - SCOPUS:85040226703

VL - 36

BT - SIGGRAPH Asia 2017 Workshops

PB - Association for Computing Machinery, Inc

Y2 - 27 November 2017 through 30 November 2017

ER -

Workshop: Data-Driven Animation Technology (D2AT)

Abstract / Description of output

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Other files and links

Fingerprint

Cite this