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. --- 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.