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cs.CV2026

Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data

Thang-Anh-Quan Nguyen, Moussab Bennehar, Luis Guillermo Roldao Jimenez +5

Cyclone is a latent diffusion framework that edits weather conditions in driving images without paired data, using cycle-consistent constraints and image‑text knowledge to produce…

cs.CV2026

SEM-ROVER: Semantic Voxel-Guided Diffusion for Large-Scale Driving Scene Generation

Hiba Dahmani, Nathan Piasco, Moussab Bennehar +5

Scalable generation of outdoor driving scenes requires 3D representations that remain consistent across multiple viewpoints and scale to large areas. Existing solutions either rely…

cs.CV2026

Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +5

Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice t…

cs.CV2026

BEDS : Bayesian Emergent Dissipative Structures : A Formal Framework for Continuous Inference Under Energy Constraints

Laurent Caraffa

We introduce BEDS (Bayesian Emergent Dissipative Structures), a formal framework for analyzing inference systems that must maintain beliefs continuously under energy constraints. U…

cs.CV2025

Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis

Thang-Anh-Quan Nguyen, Nathan Piasco, Luis Roldão +5

Synthesizing extrapolated views remains a difficult task, especially in urban driving scenes, where the only reliable sources of data are limited RGB captures and sparse LiDAR poin…

cs.CV2025

RefinedFields: Radiance Fields Refinement for Planar Scene Representations

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +3

Planar scene representations have recently witnessed increased interests for modeling scenes from images, as their lightweight planar structure enables compatibility with image-bas…