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Lagrangian--Hamiltonian Flows for Video Prediction and Image Generation: A Symplectic Perspective

Paper recorded by Signals 4 on 2026-09-28 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.CV · 计算机视觉 · first seen 2026-09-29

Abstract

We introduce LHFM, a geometric framework for learning image dynamics. Drawing on structures central to classical mechanics, symplectic geometry, and geometric quantization, LHFM represents each image as an exact Lagrangian graph and models its evolution through image-dependent Hamiltonian flows, which yield a transport--source parameterization of image velocities. Our primary application is determ

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#8 most recent of 342 cs.CV papers we have recorded · ↑ newer: Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vis · ↓ older: Mind the RefGAP: Correcting Reference Attention in Diffusion-Based Vis
Cite this page: Lagrangian--Hamiltonian Flows for Video Prediction and Image Generation: A Symplectic Perspective: the #8 most recent of 342 cs.CV papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/lagrangian-hamiltonian-flows-for-video-prediction-and-image-generation-a-symplec.html
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