Action snapshot with single pose and viewpoint
Authors: Wang, M., Guo, S., Liao, M., He, D., Chang, J. and Zhang, J.
Journal: Visual Computer
Volume: 35
Issue: 4
Pages: 507-520
ISSN: 0178-2789
DOI: 10.1007/s00371-018-1479-9
Abstract:Many art forms present visual content as a single image captured from a particular viewpoint. How to select a meaningful representative moment from an action performance is difficult, even for an experienced artist. Often, a well-picked image can tell a story properly. This is important for a range of narrative scenarios, such as journalists reporting breaking news, scholars presenting their research, or artists crafting artworks. We address the underlying structures and mechanisms of a pictorial narrative with a new concept, called the action snapshot, which automates the process of generating a meaningful snapshot (a single still image) from an input of scene sequences. The input of dynamic scenes could include several interactive characters who are fully animated. We propose a novel method based on information theory to quantitatively evaluate the information contained in a pose. Taking the selected top postures as input, a convolutional neural network is constructed and trained with the method of deep reinforcement learning to select a single viewpoint, which maximally conveys the information of the sequence. User studies are conducted to experimentally compare the computer-selected poses and viewpoints with those selected by human participants. The results show that the proposed method can assist the selection of the most informative snapshot effectively from animation-intensive scenarios.
https://eprints.bournemouth.ac.uk/30541/
Source: Scopus
Action snapshot with single pose and viewpoint
Authors: Wang, M., Guo, S., Liao, M., He, D., Chang, J. and Zhang, J.
Journal: VISUAL COMPUTER
Volume: 35
Issue: 4
Pages: 507-520
eISSN: 1432-2315
ISSN: 0178-2789
DOI: 10.1007/s00371-018-1479-9
https://eprints.bournemouth.ac.uk/30541/
Source: Web of Science (Lite)
Action snapshot with single pose and viewpoint
Authors: Wang, M., Guo, S., Liao, M., He, D., Chang, J. and Zhang, J.J.
Journal: Visual Computer
Volume: 35
Issue: 4
Pages: 507-520
ISSN: 0178-2789
Abstract:Many art forms present visual content as a single image captured from a particular viewpoint. How to select a meaningful representative moment from an action performance is difficult, even for an experienced artist. Often, a well-picked image can tell a story properly. This is important for a range of narrative scenarios, such as journalists reporting breaking news, scholars presenting their research, or artists crafting artworks. We address the underlying structures and mechanisms of a pictorial narrative with a new concept, called the action snapshot, which automates the process of generating a meaningful snapshot (a single still image) from an input of scene sequences. The input of dynamic scenes could include several interactive characters who are fully animated. We propose a novel method based on information theory to quantitatively evaluate the information contained in a pose. Taking the selected top postures as input, a convolutional neural network is constructed and trained with the method of deep reinforcement learning to select a single viewpoint, which maximally conveys the information of the sequence. User studies are conducted to experimentally compare the computer-selected poses and viewpoints with those selected by human participants. The results show that the proposed method can assist the selection of the most informative snapshot effectively from animation-intensive scenarios.
https://eprints.bournemouth.ac.uk/30541/
Source: BURO EPrints