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Chat-Edit-3D++: Interactive 3D and 4D Scene Editing via Large Language Models

Shuangkang Fang, Yufeng Wang, Yi-Hsuan Tsai, Wenrui Ding, Yi Yang, Shuchang Zhou, Ming-Hsuan Yang

arXiv:2608.29137v1cs.CV

TL;DR

Open-ended 3D/4D scene-editing dialogue demands diverse tools, while existing methods couple 2D editing to reconstruction in intricate pipelines. CE3D++ uses 2D atlases and an LLM dialogue framework to manage visual tools, reporting rich editing effects and conversational capabilities.

  • Problem

    Open-ended dialogue demands diverse editing approaches, while existing text-driven 3D editing frameworks use intricate coupled pipelines to integrate 2D models with 3D reconstruction.

    Source: §1 · p. 1

  • Method

    Hash-Atlas maps 3D and 4D scenes to 2D atlases to decouple editing from reconstruction, while CE3D++ uses an LLM-based dialogue framework to manage visual tools.

    Source: §1 · p. 2

  • Results

    CE3D++ provides rich editing effects and conversational editing capabilities, while expanding supported visual models from 20 to 30+.

    Source: §1 · p. 2, §5 · p. 14

  • Takeaways & Limitations

    Representing scenes as 2D atlases facilitates compatibility with a wide range of visual models for interactive editing of real-world scenes.

    Source: §5 · p. 14

    CE3D++ is primarily optimized for forward-facing, object-centric scenes; multiple dynamic objects entering or exiting can make pivot-view selection ambiguous and atlas representations unstable.

    Source: §4.6 · p. 13

Abstract

from arXiv · show

Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still have certain shortcomings, hindering their further development as interactive design tools. Such schemes typically adhere to fixed input patterns, limiting flexibility in text input. Furthermore, their editing capabilities are constrained by a single or a few 2D visual models and require intricate pipeline design to integrate these models into 3D reconstruction processes. To address the aforementioned issues, we propose the Hash-Atlas network, which reformulates 3D scene editing as operations on 2D atlas images, thereby achieving a workflow decoupling of the 2D editing and 3D reconstruction processes. Building on this foundation, we introduce a dialogue-based 3D scene editing approach, termed CE3D++, which is centered on a large language model (LLM) that allows arbitrary textual input from users and interprets their intentions, subsequently facilitating the autonomous invocation of the corresponding visual models. Additionally, we extend CE3D++ to monocular 4D scenes by imposing motion constraints on moving objects and further fine-tuning the LLM by creating a trajectory dataset related to editing tasks, which enables the smaller LLM to schedule up to 30 different visual tools accurately. Experimental results demonstrate that CE3D++ effectively integrates multiple visual models to achieve diverse visual editing effects, possessing strong scene comprehension and multi-round dialog capabilities. The source codes and trained models are available at https://github.com/Fangkang515/CE3D.

1 INTRODUCTION

CE3D++ addresses the difficulty of flexibly integrating visual tools into dialogue-based 3D editing by decoupling 2D atlas editing from 3D reconstruction. It extends this approach to monocular 4D scenes and uses an LLM to manage editing tools through dialogue.

Introduction

  • Existing 3D editing frameworks couple 2D visual editing with 3D reconstruction, making their pipelines intricate and hindering flexible integration of diverse tools.Open-ended dialogue can produce diverse editing requests that a single or small set of methods may not adequately handle.Source: §1 · p. 1
  • For monocular 4D editing, CE3D++ uses a pivot frame with the target’s largest visible range, atlas positional constraints, and motion loss to mitigate quality degradation from motion occlusion.The extension addresses the limited compatibility with dynamic 4D scenes of the earlier conference work.Source: §1 · pp. 1–2
  • A trajectory-tuning dataset helps smaller LLMs interpret intricate commands and schedule 30+ visual tools; experiments report compatibility with existing 2D, 3D, and 4D models and richer editing.The paper also reports more robust text parsing and more natural interactions than previous methods.Source: §1 · p. 2
  • Hash-Atlas maps views of a 3D scene to plane atlases, recasting scene editing as 2D atlas operations decoupled from reconstruction and compatible with diverse visual models.The atlas representation avoids additional designs to adapt 2D models to the 3D reconstruction process.Source: §1 · pp. 1–2
  • CE3D++ uses an LLM dialogue framework to parse user text, formulate responses, execute atlas edits, and manage multiple visual models and scene files.The interactive interface supports complex 3D editing tasks through natural-language dialogue.Source: §1 · pp. 1–2

2 RELATED WORK

Prior work provides image-editing models and LLM-based visual tool use, while 3D and 4D editing methods remain constrained by coupled pipelines, fixed representations, or limited prompt flexibility.

Vision-Language Pretrained Models

  • Vision-language pretraining,, [25],,,, [29],,, [32] supports downstream tasks; ControlNet, InstructPix2Pix [35], and BLIP-diffusion [36] enable conditional generation, instruction editing, and reference-style transfer.Combining CLIP with diffusion is also described as enabling high-fidelity text-based image generation,,, [33],, [32].Source: §2 · pp. 2–3

LLMs for Vision Tasks

  • Because LLMs cannot directly process visual input, methods map visuals to text [41],,, [44], or call visual tools [9], [10], [46]; Chat-3D [47] targets scene understanding, while 3D-GPT [48] generates scenes.This paper explores LLM assistance for editing 3D and 4D scenes.Source: §2 · p. 3

3D Scene Editing

  • CLIP-NeRF, IN2N [13], Nerf-Art [16], and DN2N integrate pretrained vision-language models into 3D editing, but intricate pipelines and rigid text inputs hinder modification and flexibility.Earlier approaches also used structural representations, NeRF editing and transformations, reference-image style transfer, and Gaussian-splatting.Source: §2 · p. 3

4D Scene Editing

  • 4D-Editor [77], Control4D, IN4Dto4D, and CTRL-D advance localized or temporally consistent editing, yet predefined representations and 2D models limit flexibility.Their mechanisms include user-provided strokes, Gaussian Triplanes with GANs, anchor-aware attention, and propagating single-frame edits across a scene.Source: §2 · p. 3

3 PROPOSED METHOD

CE3D++ moves scene editing into 2D atlases that can be mapped back to 3D or 4D views, while an LLM coordinates visual tools through dialogue. Its method combines Hash-Atlas mapping, atlas-space editing, and a tuned dialogue system.

3.1 Hash-Atlas Model

  • Hash-Atlas maps each scene-view pixel to foreground and background UV coordinates, predicts atlas colors with a shared hash network, and reconstructs edited views without retraining.The mapping also predicts foreground transparency; the two UV spaces occupy separate halves of the shared network’s interval.Source: §3.1 · p. 3
  • Training combines initial positional and foreground-mask supervision with reconstruction, inpainting, rigid, and flow constraints to limit atlas distortion and background omissions.For 4D scenes, a pivot-motion loss initializes from the view with the most object-mask pixels and aligns tracked points to consistent atlas coordinates, reducing tracking inconsistencies.Source: §3.1 · pp. 3, 5

3.2 Editing in Atlas Space

  • The merge-split strategy edits background-only regions directly, but overlays foreground and background atlases for other edits before separating the result with the original and new object masks.This addresses incomplete scene information in individual atlases, especially sparse foreground atlases, which can hinder visual models’ semantic understanding and editing accuracy.Source: §3.2 · pp. 5–6

3.1 Hash-Atlas Model

  • Compared with LNA [82], Hash-Atlas improves PSNR by 1.1∼5.1dB, accelerates training by 14.2∼18.6x, and increases FPS by 7.6∼9.0x.The table reports averages over scenes from various datasets.Source: Table 1 · p. 6

3.3 Dialog System

  • The dialogue system uses opaque scene-name strings and annotated tool descriptions to guide tool selection, then fine-tunes LLMs on 1,000 dialogue samples for tool orchestration.The targeted tuning improves tool-use accuracy, reduces failures in multi-step edits, and preserves scheduling accuracy when new tools absent from the training data are introduced.Source: §3.3 · pp. 6, 9

4 EXPERIMENTS AND ANALYSIS

The experiments report improved atlas reconstruction with Hash-Atlas and broader editing capabilities with CE3D++ than baseline methods. They also assess trajectory tuning, component contributions, and limitations across 3D and 4D scenes.

4.1 Implementation Details

  • Experiments use LLFF, NeRF-Art, IN2N-collect, IBRNet-collect, TanksAndTemple, CE3D-collect, DyCheck, and DynamicNeRF; ChatGPT is the default LLM, with other commercial and open models evaluated.CE3D-collect contains 11 forward-facing outdoor scenes with 50 to 80 images each.Source: §4.1 · p. 9

4.2 Atlas Reconstruction Results

  • Hash-Atlas significantly outperforms LNA, using multiresolution hash encoding to accelerate training and inference, and dedicated pre-training and inpainting losses to address distortions under sparsely sampled, non-continuous viewpoints.In a three-atlas setting, Hash-Atlas produces higher-quality atlas representations than LNA, with fewer visual artifacts.Source: §4.2 · p. 10, Figure 11 · p. 10

4.3 Editing Results and Comparisons

  • Across a 12-round example, CE3D++ handles free-form requests spanning object edits, style transfer, depth prediction, scene regeneration, scene VQA, and basic dialogue.Its compatibility with varied 2D visual models allows editing capabilities to expand as new models become available.Source: §4.3 · p. 10
  • CE3D++ supports more diverse editing functions than 3D and 4D baselines and excels at matching text descriptions with competitive editing efficiency and GPU memory use.The quantitative comparison evaluates CLIP similarity, CLIP Directional Score, editing time, and peak VRAM; memory comparisons load only tools needed for each task.Source: §4.3 · pp. 10–11, Table 2 · p. 10
  • CE3D++ handles diverse textual queries more robustly than IN2N [13] and performs better in four-round editing dialogue, where IN2N [13] struggles with complex interactions.The paper attributes this to LLM-based instruction interpretation and broader functionality.Source: §4.3 · p. 11

4.4 Effectiveness of Trajectory-tuning

  • Trajectory tuning significantly improves smaller LLaMA-8B and Qwen-14B tool-calling performance, especially for complex multi-step instructions where errors can cascade.Unlike few-shot prompting, it internalizes planning in model parameters, reducing reliance on long in-context demonstrations and supporting low-cost local deployment.Source: Figure 12 · p. 10, §4.4 · p. 12

4.6 Discussions and Limitations

  • CE3D++ depends on correct LLM parsing, prompt tuning, effective external tools, and reliable tracking; 360-degree distortion and rapid deformation or severe occlusion can produce unstable atlases or editing artifacts.The method is primarily optimized for forward-facing and object-centric scenes, and multiple dynamic objects entering or leaving the frame can make pivot-view selection ambiguous.Source: §4.6 · p. 13

5 CONCLUSIONS

CE3D++ uses 2D scene atlases to connect 3D/4D scene editing with diverse 2D visual models, while LLMs manage visual tools and scene files for conversational editing. The work advances adaptable interactive scene editing but notes that atlas optimization in complex 360-degree environments remains open.

5 CONCLUSIONS

  • Representing 3D/4D scenes as 2D atlases makes scene editing compatible with a wide range of 2D visual models.The atlases map scene views onto 2D plane images.Source: §5 · p. 14
  • LLMs manage visual tools and scene files to give CE3D++ rich editing effects and sustained conversational editing capabilities.The work also explores and proposes solutions to improve LLM stability.Source: §5 · p. 14
  • Atlas optimization for complex 360-degree environments remains an opportunity for further progress.The work advances interactive editing of real-world scenes beyond restricted visual models toward a more adaptable framework.Source: §5 · p. 14

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