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BEGIN:VEVENT
DTSTAMP:20250522T212944Z
LOCATION:Mile High 4
DTSTART;TZID=America/Denver:20240731T095000
DTEND;TZID=America/Denver:20240731T100000
UID:siggraph_SIGGRAPH 2024_sess126_papers_880@linklings.com
SUMMARY:Physics-informed Learning of Characteristic Trajectories for Smoke
  Reconstruction
DESCRIPTION:Yiming Wang and Siyu Tang (ETH Zürich) and Mengyu Chu (Peking 
 University, National Key Lab of General AI)\n\nWe introduce Neural Charact
 eristic Trajectory Fields, a novel representation utilizing Eulerian neura
 l fields to implicitly model Lagrangian fluid trajectories for video-based
  fluid reconstruction. This topology-free, auto-differentiable representat
 ion facilitates end-to-end supervision, encompassing long-term conservatio
 n and short-term physics priors. It offers advancements in high-fidelity f
 luid reconstruction across synthetic and real scenes.\n\nInterest Area: Re
 search & Education\n\nRecording: Livestreamed, Recorded\n\nKeyword: Animat
 ion, Modeling\n\nRegistration Category: Full Conference, Full Conference S
 upporter, Virtual Access, Exhibitor Full Conference, Wednesday\n\n
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