[talks] L Luo preFPO

[Melissa M. Lawson]

Linjie Luo will present his preFPO on Thursday December 13 at 11AM in 
Room 402. The members of his committee are: Szymon Rusinkiewicz, 
advisor; Tom Funkhouser and Sylvain Paris (Adobe), readers; Adam 
Finkelstein and David Dobkin, nonreaders. Everyone is invited to attend 
his talk. His abstract follows below. 

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Title: Towards High Quality Hair Capture and Modeling 


Abstract: 


Hair is one of human's most distinctive features and one important 
component in human digitalization. However, capturing and modeling 
hair remains challenging because of hair's unique properties: the 
specular appearance violates the Lambertian surface assumption used in 
most multi-view stereo reconstruction methods; The vast variations and 
complex topology of hair styles pose great technical difficulties for 
high quality hair capture and modeling. 


We propose an orientation-based matching metric for multi-view hair 
capture that is more robust to hair's view-dependent specular 
highlights. The metric is performed in multi-resolution to reveal 
increasing hair structures and details. The orientation-based metric 
also facilitates the structure-aware aggregation to reduce matching 
error and stereo noise along coherent hair structures. Our approach is 
able to reconstruct approximate surfaces to a variety of hair styles 
with detailed hair structures from ~30 input views. 


To alleviate the requirement for dense small-baseline capture setup, 
we introduce a visual hull based refinement method to enable 
wide-baseline full hair capture with only 8 views. The refinement is 
driven by the strands generated from the orientation map of each input 
view. The final shape is obtained by optimizing the orientation 
consistency of these strands against all the orientation maps as well 
as the regularization terms that account for the smoothness at strand, 
wisp and global levels. We find our method effective on various hair 
styles and suitable for dynamic hair capture. We achieve an 
average reconstruction accuracy of ~3mm on synthetic datasets. 


Finally, we address the challenge of robustly modeling hair strands 
from the captured incomplete exterior hair geometry with plausible 
topology for hair simulation. Our main contribution is the 
construction and analysis of the global connection graph to compute 
the plausible hair topology from the incomplete exterior hair 
geometry. The connection graph encodes coherent local strands and 
plausible inter-strand connections. The final hair strands can be 
generated from a wisp forest derived from the connection graph. 



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