Source-linked AI summary
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
TL;DR
Existing text-driven 3D editing methods restrict input flexibility and tightly couple limited visual models to reconstruction pipelines. The paper introduces Hash-Atlas and CE3D++, extending the framework to monocular 4D editing and LLM-based tool orchestration. CE3D++ integrates diverse visual models for rich editing and multi-round dialogue, while remaining limited by LLM parsing, external tool quality, and complex-scene atlas issues.
Problem
Existing methods use fixed input patterns, limited visual models, and intricate 2D–3D integration designs, constraining interactive scene editing.
Method
Hash-Atlas converts 3D and 4D scene editing into 2D atlas operations, while CE3D++ uses an LLM to interpret dialogue and manage visual tools and scene files.
Results
CE3D++ is reported to integrate multiple visual models, support diverse editing effects, and provide strong scene comprehension and multi-round dialogue capabilities.
Takeaways & Limitations
The atlas representation facilitates compatibility between scene editing and a wide range of 2D visual models, supporting interactive editing of real-world scenes.
Takeaways & Limitations
Performance depends on correct LLM parsing and external visual tools, while complex dynamic or 360-degree scenes can produce ambiguous pivot selection or distorted atlases.
Abstract
from arXiv · showhide
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
The paper identifies rigid, tightly coupled text-driven editing pipelines as barriers to flexible interactive 3D and 4D scene editing. It proposes Hash-Atlas and CE3D++ to decouple atlas editing from reconstruction and coordinate diverse visual tools through dialogue.
- Motivation: Existing frameworks couple specific 2D editing models with 3D reconstruction through iterative, supervised, or denoised designs.These approaches require intricate integration and model selection, limiting flexible incorporation of visual tools.
- Hash-Atlas: Hash-Atlas maps multiple scene views to 2D plane atlases, reformulating 3D and 4D editing as atlas-space manipulation.This separates the 2D editing stage from the 3D reconstruction pipeline.
- 4D Extension: The method extends to monocular 4D editing by selecting a visible pivot frame, applying positional constraints, and introducing motion loss for moving objects.These components address motion occlusion and support mapping dynamic scenes to atlases.
- CE3D++: CE3D++ uses an LLM dialogue framework to parse user text, formulate responses, execute atlas editing, and manage visual models and scene files.The framework is designed for complex interactive scene-editing tasks through natural-language interaction.
- Results: CE3D++ supports more than 30 visual tools after trajectory tuning and is reported to provide stronger text parsing, richer editing, and more natural interactions than previous methods.The experiments also report compatibility with existing 2D, 3D, and 4D visual models.
2 RELATED WORK
The related work spans vision-language models for controllable image generation and editing, LLMs that invoke visual tools, and neural representations for editing static and dynamic 3D scenes.
- Vision-Language Models: Vision-language pretraining has supported downstream visual tasks, including text-based image generation and controllable image editing.Examples include ControlNet, InstructPix2Pix, and BLIP-diffusion.
- LLMs for Vision Tasks: Because LLMs cannot inherently process visual input, prior systems map visual information into text or use LLMs as inference hubs that call visual tools.Visual-ChatGPT and MM-REACT exemplify tool-invocation approaches.
- 3D Scene Editing: 3D scene-editing research has used structural representations such as point clouds and meshes, followed by methods based on NeRF and related representations.These methods support implicit modeling and structural transformations for diverse editing capabilities.
- 4D Scene Editing: 4D scene representations model dynamic object structure through deformation-based extensions of NeRF or Gaussian splatting.Recent 4D editing methods build on these dynamic representations to enable localized scene editing.
3 PROPOSED METHOD
The proposed method moves 3D and 4D editing into 2D atlas space, then uses a dialogue agent to coordinate visual tools and scene files. Hash-Atlas supports reconstruction from edited atlases, while motion constraints and trajectory tuning extend the workflow to dynamic scenes and complex tool scheduling.
- Hash-Atlas Model: Hash-Atlas maps scene views to foreground and background atlases, relocating 3D and 4D editing to 2D operations and decoupling editing from reconstruction.The network is designed to minimize atlas distortion and preserve scene structure during coordinate mapping.
- Hash-Atlas Model: Edited atlases can be mapped back to every scene view through Eq. (3) without retraining Hash-Atlas.This enables visual edits to be restored in the original 3D or 4D views after atlas-space processing.
- Dialog System: CE3D++ has an LLM interpret queries, select and parameterize tools, coordinate backend files, and repeat tool calls until editing is complete.The customized agent uses tool descriptions, examples, thought processes, and trajectory tuning to improve complex instruction handling and scheduling reliability.
- Dialog System: The system separates scene files from language reasoning through unique meaningless .scn identifiers that the frontend and backend resolve into real files.This representation prevents fabricated scene names while allowing the workflow to manage generated and existing scenes.
- Hash-Atlas Model: The training objective combines reconstruction, rigid, and flow constraints, while inpainting loss addresses omissions in the background atlas.Rigid constraints preserve relative point locations, and flow constraints align corresponding points across views on the atlas.
- Hash-Atlas Model: For dynamic scenes, pivot-motion loss initializes moving-object positions from the view with the largest visible object area and reduces tracking inconsistencies.The loss stabilizes moving-object representations in atlas space across frames where key points are successfully tracked.
4 EXPERIMENTS AND ANALYSIS
Experiments evaluate Hash-Atlas reconstruction, CE3D++ editing breadth and efficiency, dialogue stability, ablations, and limitations across 3D and monocular 4D scenes. Results support broad tool compatibility and interactive editing, while revealing dependence on scene complexity, tracking, and external tools.
- Experimental setup: The experiments compare Hash-Atlas with LNA, evaluate CE3D++ against 3D and 4D baselines, and analyze LLMs, trajectory tuning, and ablations.Datasets include LLFF, NeRF-Art, IN2N-collect, IBRNet-collect, TanksAndTemple, CE3D-collect, DyCheck, and DynamicNeRF.
- Atlas reconstruction: Hash-Atlas significantly outperforms LNA and adapts to complex LLM-driven 3D and 4D editing operations.Dedicated pre-training and inpainting losses improve geometric stability and reduce occlusion-induced artifacts.
- Editing capabilities and efficiency: CE3D++ handles 12-round free-form dialogues spanning object edits, style transfer, depth prediction, scene regeneration, visual question answering, and text dialogue.Its compatibility with varied 2D visual models enables diverse effects and extension with newly developed models.
- Editing capabilities and efficiency: CE3D++ supports broader editing functionality than baseline methods, including pose sketching, scene enhancement, defocusing, and color adjustment.The decoupled workflow allows integration of diverse external visual tools rather than relying on a single 2D editing model.
- Dialogue performance: LLM-based interpretation improves matching of intended text and supports more stable multi-turn editing than IN2N for diverse textual queries.The baseline struggles with complex multi-turn tasks because of limited editing functionality and inadequate text interpretation.
- LLM analysis: Early-stage tool-scheduling errors can propagate into cascading failures, while few-shot prompting provides no significant accuracy gains under the limited context window.The setting requires decomposing intent, invoking multiple tools in order, and reusing intermediate results.
- Ablation studies: Removing reconstruction losses decreases edited-result quality and rationality, while omitting scene-name management or atlas merging can cause incorrect or incomplete edits.The Executor is needed to preserve complete foreground and background scene information during atlas editing.
- 4D analysis: 4D atlas performance is affected by long sequences, severe occlusions, rapid motion, and inaccurate tracking.Pivot-frame selection performs better when the selected frame provides larger visible object area and more structural coverage.
5 CONCLUSIONS
The conclusion presents 2D atlas representations and LLM-managed visual tools as a route to adaptable, conversational editing of real-world 3D and 4D scenes. It also identifies atlas optimization for complex 360-degree environments as an area for further progress.
- Conclusion: Hash-Atlas maps 3D or 4D scene views to 2D plane images, decoupling scene editing from reconstruction and broadening compatibility with 2D visual models.This representation supports richer editing effects and sustainable conversational editing through LLM management of tools and scene files.
- Conclusion: The paper advances interactive real-world scene editing beyond restricted visual models while noting the need for further atlas optimization in complex 360-degree environments.