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Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects

Authors :
Barda, Amir
Gadelha, Matheus
Kim, Vladimir G.
Aigerman, Noam
Bermano, Amit H.
Groueix, Thibault
Publication Year :
2024

Abstract

We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without the need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.<br />Comment: project page: https://amirbarda.github.io/Instant3dit.github.io/

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2412.00518
Document Type :
Working Paper