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20232026
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cs.CV2026

SphericalDreamer: Generating Navigable Immersive 3D Worlds with Panorama Fusion

Antoine Schnepf, Karim Kassab, Flavian Vasile +1

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 fund…

cs.CV2024

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.CV2024

Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder

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

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 red…

cs.CV2024

Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes

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

We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes. We combine two techniques to improve the required training time and memory…

cs.CV2023

3DGEN: A GAN-based approach for generating novel 3D models from image data

Antoine Schnepf, Flavian Vasile, Ugo Tanielian

The recent advances in text and image synthesis show a great promise for the future of generative models in creative fields. However, a less explored area is the one of 3D model ge…

cs.CV2023

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…