Flexible sensors hold promise for human motion capture (MoCap), offering advantages such as wearability, privacy preservation, and minimal constraints on natural movement. However, existing flexible sensor based MoCap...
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Flexible sensors hold promise for human motion capture (MoCap), offering advantages such as wearability, privacy preservation, and minimal constraints on natural movement. However, existing flexible sensor based MoCap methods rely on deep learning and necessitate large and diverse labeled datasets for training. These data typically need to be collected in MoCap studios with specialized equipment and substantial manual labor, making them difficult and expensive to obtain at scale. Thanks to the high-linearity of flexible sensors, we address this challenge by proposing a novel Sim2Real solution for hinge joint tracking based on domain adaptation, eliminating the need for labeled data yet achieving comparable accuracy to supervised learning. Our solution relies on a novel Support-based Domain Adaptation method, namely SuDA, which aligns the supports of the predictive functions rather than the instance-dependent distributions between the source and target domains. Extensive experimental results show the effectiveness of our method and its superiority over state-of-the-art distribution-based domain adaptation methods in our task. Copyright 2024 by the author(s)
Role–event videos are rich in information but challenging to be understood at the story *** social roles and behavior patterns of characters largely depend on the interactions among characters and the background *** ...
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Role–event videos are rich in information but challenging to be understood at the story *** social roles and behavior patterns of characters largely depend on the interactions among characters and the background *** them requires analysis of the video contents for a long duration,which is beyond the ability of current algorithms designed for analyzing short-time *** this paper,we propose In Social Net,an interactive video analytics tool for analyzing the contents of role–event *** automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition,and provides a visual interface for interactive analysis of video *** with social network analysis at the back end,In Social Net supports users to investigate characters,their relationships,social roles,factions,and events in the input *** conduct case studies to demonstrate the effectiveness of In Social Net in assisting the harvest of rich information from role–event *** believe the current prototype implementation can be extended to applications beyond movie analysis,e.g.,social psychology experiments to help understand crowd social behaviors.
Boolean games offer a compact alternative to normal-form games, by encoding the goal of each agent as a propositional formula. In this paper, we show how this framework can be naturally extended to model situations in...
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