Antoine Schnepf — accepted AI-conference papers

2026

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

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Flavian Vasile, Jeremie Mary +2

ICLR 2026poster

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 to model large collections of 3D objects. However, training Tri-Planes to model such large collections is computationally…

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2026

SphericalDreamer: Generating Navigable Immersive 3D Worlds with Panorama Fusion

Antoine Schnepf, Karim Kassab, Flavian Vasile, Andrew Comport

ICML 2026poster

The generation of immersive and navigable 3D environments is increasingly prevalent with the growing adoption of virtual reality and 3D content. However, recent methods face a fundamental limitation: they cannot produce 3D worlds that simultaneously (i) are navigable over long-range spatial extents …

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2025

Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder

Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian Vasile, Jeremie Mary +2

ICLR 2025poster

While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interopera…

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