AI RESEARCH
GeodesicNVS: Probability Density Geodesic Flow Matching for Novel View Synthesis
arXiv CS.CV
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ArXi:2603.01010v2 Announce Type: replace Recent advances in generative modeling have substantially enhanced novel view synthesis, yet maintaining consistency across viewpoints remains challenging. Diffusion-based models rely on stochastic noise-to-data transitions, which obscure deterministic structures and yield inconsistent view predictions. We advocate a Data-to-Data Flow Matching framework that learns deterministic transformations between paired views, enhancing view-consistent synthesis through explicit data coupling.