Document Detail


Analyzing Locomotion Synthesis with Feature-Based Motion Graphs.
MedLine Citation:
PMID:  22752722     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of post-processing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.
Authors:
Mentar Mahmudi; Marcelo Kallmann
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-6-27
Journal Detail:
Title:  IEEE transactions on visualization and computer graphics     Volume:  -     ISSN:  1941-0506     ISO Abbreviation:  IEEE Trans Vis Comput Graph     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-7-3     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9891704     Medline TA:  IEEE Trans Vis Comput Graph     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
University of California, Merced, Merced.
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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