Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training

• URL: http://arxiv.org/abs/2312.01663v1
• Date: Mon, 4 Dec 2023 06:25:06 GMT
• Title: Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
• Authors: Runze He, Shaofei Huang, Xuecheng Nie, Tianrui Hui, Luoqi Liu, Jiao Dai, Jizhong Han, Guanbin Li, Si Liu
• Abstract summary: We propose a CustomNeRF model that unifies a text description or a reference image as the editing prompt. To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing. For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem.
• Score: 61.984277261016146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt. However, obtaining desired editing results conformed with the editing prompt is nontrivial since there exist two significant challenges, including accurate editing of only foreground regions and multi-view consistency given a single-view reference image. To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing, aimed at foreground-only manipulation while preserving the background. For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem among different views in image-driven editing. Extensive experiments show that our CustomNeRF produces precise editing results under various real scenes for both text- and image-driven settings.

Related papers

• GenVideo: One-shot Target-image and Shape Aware Video Editing using T2I Diffusion Models [2.362412515574206]
  We propose "GenVideo" for editing videos leveraging target-image aware T2I models. Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit.
  arXiv  Detail & Related papers  (2024-04-18T23:25:27Z)
• TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts [119.84478647745658]
  TIPEditor is a 3D scene editing framework that accepts both text and image prompts and a 3D bounding box to specify the editing region. Experiments have demonstrated that TIP-Editor conducts accurate editing following the text and image prompts in the specified bounding box region.
  arXiv  Detail & Related papers  (2024-01-26T12:57:05Z)
• Free-Editor: Zero-shot Text-driven 3D Scene Editing [8.966537479017951]
  Training a diffusion model specifically for 3D scene editing is challenging due to the scarcity of large-scale datasets. We introduce a novel, training-free 3D scene editing technique called textscFree-Editor, which enables users to edit 3D scenes without the need for model retraining. Our method effectively addresses the issue of multi-view style inconsistency found in state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2023-12-21T08:40:57Z)
• Optimisation-Based Multi-Modal Semantic Image Editing [58.496064583110694]
  We propose an inference-time editing optimisation to accommodate multiple editing instruction types. By allowing to adjust the influence of each loss function, we build a flexible editing solution that can be adjusted to user preferences. We evaluate our method using text, pose and scribble edit conditions, and highlight our ability to achieve complex edits.
  arXiv  Detail & Related papers  (2023-11-28T15:31:11Z)
• Object-aware Inversion and Reassembly for Image Editing [61.19822563737121]
  We propose Object-aware Inversion and Reassembly (OIR) to enable object-level fine-grained editing. We use our search metric to find the optimal inversion step for each editing pair when editing an image. Our method achieves superior performance in editing object shapes, colors, materials, categories, etc., especially in multi-object editing scenarios.
  arXiv  Detail & Related papers  (2023-10-18T17:59:02Z)
• Editing 3D Scenes via Text Prompts without Retraining [80.57814031701744]
  DN2N is a text-driven editing method that allows for the direct acquisition of a NeRF model with universal editing capabilities. Our method employs off-the-shelf text-based editing models of 2D images to modify the 3D scene images. Our method achieves multiple editing types, including but not limited to appearance editing, weather transition, material changing, and style transfer.
  arXiv  Detail & Related papers  (2023-09-10T02:31:50Z)
• SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field [37.8162035179377]
  We present a novel semantic-driven NeRF editing approach, which enables users to edit a neural radiance field with a single image. To achieve this goal, we propose a prior-guided editing field to encode fine-grained geometric and texture editing in 3D space. Our method achieves photo-realistic 3D editing using only a single edited image, pushing the bound of semantic-driven editing in 3D real-world scenes.
  arXiv  Detail & Related papers  (2023-03-23T13:58:11Z)
• Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions [109.51624993088687]
  We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.
  arXiv  Detail & Related papers  (2023-03-22T17:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.