title: SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds
summary: We propose a novel feed-forward 3D editing framework called Shap-Editor.
  Prior research on editing 3D objects primarily concentrated on editing
  individual objects by leveraging off-the-shelf 2D image editing networks. This
  is achieved via a process called distillation, which transfers knowledge from
  the 2D network to 3D assets. Distillation necessitates at least tens of minutes
  per asset to attain satisfactory editing results, and is thus not very
  practical. In contrast, we ask whether 3D editing can be carried out directly
  by a feed-forward network, eschewing test-time optimisation. In particular, we
  hypothesise that editing can be greatly simplified by first encoding 3D objects
  in a suitable latent space. We validate this hypothesis by building upon the
  latent space of Shap-E. We demonstrate that direct 3D editing in this space is
  possible and efficient by building a feed-forward editor network that only
  requires approximately one second per edit. Our experiments show that
  Shap-Editor generalises well to both in-distribution and out-of-distribution 3D
  assets with different prompts, exhibiting comparable performance with methods
  that carry out test-time optimisation for each edited instance.
paper_authors._id: 657bc2aae34a7de14b0b3b1f
paper_authors.name: Minghao Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-15T16:14:55.226Z
paper_authors._id: 636a3d8bf8d9af4aea18553f
paper_authors.avatarUrl: /avatars/028a86d088764fd66c36a2ddebf09f9a.svg
paper_authors.fullname: MINGHAO CHEN
paper_authors.type: user
paper_authors.user: silentchen
paper_authors._id: 657bc2aae34a7de14b0b3b20
paper_authors.name: Junyu Xie
paper_authors._id: 657bc2aae34a7de14b0b3b21
paper_authors.name: Iro Laina
paper_authors._id: 657bc2aae34a7de14b0b3b22
paper_authors.name: Andrea Vedaldi
paper_title: SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds
paper_summary: We propose a novel feed-forward 3D editing framework called Shap-Editor.
  Prior research on editing 3D objects primarily concentrated on editing
  individual objects by leveraging off-the-shelf 2D image editing networks. This
  is achieved via a process called distillation, which transfers knowledge from
  the 2D network to 3D assets. Distillation necessitates at least tens of minutes
  per asset to attain satisfactory editing results, and is thus not very
  practical. In contrast, we ask whether 3D editing can be carried out directly
  by a feed-forward network, eschewing test-time optimisation. In particular, we
  hypothesise that editing can be greatly simplified by first encoding 3D objects
  in a suitable latent space. We validate this hypothesis by building upon the
  latent space of Shap-E. We demonstrate that direct 3D editing in this space is
  possible and efficient by building a feed-forward editor network that only
  requires approximately one second per edit. Our experiments show that
  Shap-Editor generalises well to both in-distribution and out-of-distribution 3D
  assets with different prompts, exhibiting comparable performance with methods
  that carry out test-time optimisation for each edited instance.
paper_ai_summary: A feed-forward network named Shap-Editor performs efficient 3D editing in a latent space derived from Shap-E, achieving fast and high-quality editing results.
paper_ai_keywords: feed-forward 3D editing latent space distillation test-time optimisation Shap-E in-distribution out-of-distribution