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LatentEditor: Text Driven Local Editing of 3D Scenes

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15122))

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Abstract

While neural fields have made significant strides in view synthesis and scene reconstruction, editing them poses a formidable challenge due to their implicit encoding of geometry and texture information from multi-view inputs. In this paper, we introduce LatentEditor, an innovative framework designed to empower users with the ability to perform precise and locally controlled editing of neural fields using text prompts. Leveraging denoising diffusion models, we successfully embed real-world scenes into the latent space, resulting in a faster and more adaptable NeRF backbone for editing compared to traditional methods. To enhance editing precision, we introduce a delta score to calculate the 2D mask in the latent space that serves as a guide for local modifications while preserving irrelevant regions. Our novel pixel-level scoring approach harnesses the power of InstructPix2Pix (IP2P) to discern the disparity between IP2P conditional and unconditional noise predictions in the latent space. The edited latents conditioned on the 2D masks are then iteratively updated in the training set to achieve 3D local editing. Our approach achieves faster editing speeds and superior output quality compared to existing 3D editing models, bridging the gap between textual instructions and high-quality 3D scene editing in latent space. We show the superiority of our approach on four benchmark 3D datasets, LLFF [26], IN2N [8], NeRFStudio [44] and NeRF-Art [47]. Project Page: https://latenteditor.github.io/.

U. Khalid, H. Iqbal and N. Karim—Equal Contribution.

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Acknowledgement

This work was partially supported by the NSF under Grant Numbers OAC-1910469 and OAC-2311245.

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Authors and Affiliations

  1. Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA

    Umar Khalid, Nazmul Karim, Muhammad Tayyab & Chen Chen

  2. Department of Computer Science, Wayne State University, Detroit, MI, USA

    Hasan Iqbal & Jing Hua

Authors
  1. Umar Khalid
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  2. Hasan Iqbal
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  3. Nazmul Karim
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  4. Muhammad Tayyab
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  5. Jing Hua
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  6. Chen Chen
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Corresponding author

Correspondence to Umar Khalid .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Khalid, U., Iqbal, H., Karim, N., Tayyab, M., Hua, J., Chen, C. (2025). LatentEditor: Text Driven Local Editing of 3D Scenes. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15122. Springer, Cham. https://doi.org/10.1007/978-3-031-73039-9_21

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