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arxiv: 2512.17445 · v2 · submitted 2025-12-19 · 💻 cs.CV

LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents

Yun He , Francesco Pittaluga , Ziyu Jiang , Matthias Zwicker , Manmohan Chandraker , Zaid Tasneem This is my paper

Pith reviewed 2026-05-16 20:53 UTC · model grok-4.3

classification 💻 cs.CV
keywords driving scene editingnatural language controlmulti-modal agents3D scene graphsvideo diffusiontraffic scenario generationinstruction alignment
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The pith

LangDriveCTRL edits real driving videos from natural language by modeling them as 3D scene graphs and routing instructions through specialized agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LangDriveCTRL as a framework that converts natural language instructions into edited driving videos by first representing each video as an explicit 3D scene graph that separates static backgrounds from dynamic object nodes. An orchestrator then directs a sequence of multi-modal agents to ground text to specific objects, generate and refine multi-object trajectories, review outputs for consistency, and finally harmonize the results with video diffusion tools. A sympathetic reader would care because this approach supports fine-grained changes such as inserting vehicles or altering their paths while claiming nearly twice the instruction alignment of prior methods along with better photorealism and traffic realism.

Core claim

LangDriveCTRL represents each driving video as an explicit 3D scene graph that decomposes the scene into a static background and dynamic object nodes, then applies a feedback-driven agentic pipeline in which an orchestrator converts user instructions into executable graphs that coordinate an Object Grounding Agent, a Behavior Editing Agent, a Behavior Reviewer Agent, and a Video Reviewer Agent; the edited graph is rendered and refined with a video diffusion tool to produce photorealistic outputs that support both object-level edits and multi-object behavior changes from natural language.

What carries the argument

The feedback-driven agentic pipeline operating on a 3D scene graph representation, where an orchestrator coordinates object grounding, trajectory generation, iterative review, and video diffusion harmonization to translate language instructions into scene edits.

If this is right

  • Object nodes support removal, insertion, and replacement directly from text instructions.
  • Multi-object behaviors are generated as trajectories that can be iteratively reviewed and refined.
  • Final videos achieve nearly 2 times higher alignment with user instructions than prior state-of-the-art methods.
  • Photorealism, structural preservation, and traffic realism remain superior through the review and diffusion stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pipeline could generate synthetic variations of rare traffic events for training safety models without requiring new real-world captures.
  • Closed-loop integration with motion planners might create on-demand test scenarios for autonomous driving systems.
  • Extending the agents to handle longer sequences or denser urban scenes would test whether the current grounding and review steps scale without additional human oversight.
  • Analogous agentic decompositions could apply to editing other structured video domains such as surveillance footage or robotic manipulation sequences.

Load-bearing premise

The multi-modal agents and video diffusion tool can reliably ground language to objects, produce realistic trajectories, and generate photorealistic renderings that preserve scene structure without artifacts in complex real-world driving footage.

What would settle it

A side-by-side comparison on the same input videos where edited outputs show mismatched object identities, trajectories that violate road geometry or physics, or visible artifacts that reduce photorealism relative to the original footage.

Figures

Figures reproduced from arXiv: 2512.17445 by Francesco Pittaluga, Manmohan Chandraker, Matthias Zwicker, Yun He, Zaid Tasneem, Ziyu Jiang.

Figure 1
Figure 1. Figure 1: Comparison with baselines. Cosmos [2] achieves high visual quality but fails to align with the target behavior and modifies the background, showing poor controllability. While ChatSim [48] preserves background information, it suffers from poor photorealism, inaccurate trajectory generation, and traffic violation (e.g., collision). In contrast, our method achieves photorealism, instruction alignment, struct… view at source ↗
Figure 2
Figure 2. Figure 2: Overall Pipeline. Given an input video and the user instruction, our pipeline first builds a scene graph, which decomposes the scene into a static background node and multiple dynamic object nodes with their trajectories. To execute the instruction, the orchestrator coordinates agents and tools from different modules to work together: the object query module localizes target object nodes in the scene graph… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison with baselines. The results generated by Cosmos [2] fail to align with the instruction and do not preserve the background well. ChatSim [48] produces editing results with poor visual quality, inaccurate trajectories, and collision issues. Our method clearly outperforms them in photorealism, instruction alignment, structure preservation, and traffic realism. Video Diffusion Tool. To a… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative editing results. We demonstrate our method’s editing capabilities for diverse scenario generation. Note that for better visualization, the timestamps within each column are not strictly aligned. (“Make the ego vehicle change to the rightmost lane.”). We not only achieve accurate ego view changes, but also capture the surrounding environmental lighting informa￾tion (e.g., realistic highlights an… view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study for behavior feedback loop. The feedback loop effectively improves the alignment between generated trajectories and instructions while avoiding off-road behavior and collisions [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results for open vocabulary object query. The detected object masks are highlighted in red. Compared to Grounding SAM [32] and 4DLangSplat [23], our method demonstrates stronger capability in recognizing different attributes of vehicles, especially spatial information. generate edited videos of 8 seconds at 10 fps (80 frames in total). All experiments are conducted on a single NVIDIA A6000 GPU … view at source ↗
Figure 7
Figure 7. Figure 7: Extra qualitative comparison with baselines. Our method significantly outperforms previous approaches across all four aspects: photorealism, instruction alignment, structure preservation, and traffic realism. Note in the first instruction, when the newly inserted green sedan cuts in, both the ego vehicle and the green sedan recognize they are too close and decide to stop, which demonstrates that our method… view at source ↗
Figure 8
Figure 8. Figure 8: Extra qualitative results for behavior feedback loop. In iteration 1, the ego vehicle remains in its lane, while car 3 changes lanes but goes off-road. The reviewer agent then increases the classifier-free guidance weight for the ego vehicle and applies on-road guidance to car 3. In iteration 2, the ego vehicle changes lanes but fails to reach the leftmost lane in its direction, while part of car 3’s traje… view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative results for the video diffusion tool. Typically, visual quality suffers in two scenarios: 1) when viewpoints change substantially, rendering quality drops significantly; 2) when new objects are inserted, meshes appear inconsistent with the original scene. The video diffusion tool effectively addresses both issues. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Failure case: traffic violation. The newly inserted vehicle (highlighted in red) incorrectly drives on the median barrier, as the behavior editing module fails to recognize it as a non-drivable area. Input “Insert a white sedan 3 meters to the left of ego vehicle, 6 meters ahead, and make it change to the right lane.” Ours Input After VDM “Insert a green convertible 3 meters to the left of ego vehicle, 6 … view at source ↗
Figure 11
Figure 11. Figure 11: Failure case: vehicle appearance change. The video diffusion model changes the inserted green convertible into a black sedan during refinement, as it was trained primarily on common vehicle types and colors. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Orchestrator prompt. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Object grounding agent prompt. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Insertion agent prompt for mesh scaling and coordinate transformation. [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Behavior editing agent prompt for selecting counterfactual behaviors. [PITH_FULL_IMAGE:figures/full_fig_p031_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Behavior reviewer agent prompt. 33 [PITH_FULL_IMAGE:figures/full_fig_p033_16.png] view at source ↗
read the original abstract

LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D scene graph, decomposing the scene into a static background and dynamic object nodes. To enable fine-grained editing and realism, it introduces a feedback-driven agentic pipeline. An Orchestrator converts user instructions into executable graphs that coordinate specialized multi-modal agents and tools. An Object Grounding Agent aligns free-form text with target object nodes in the scene graph; a Behavior Editing Agent generates multi-object trajectories from language instructions; and a Behavior Reviewer Agent iteratively reviews and refines the generated trajectories. The edited scene graph is rendered and harmonized using a video diffusion tool, and then further refined by a Video Reviewer Agent to ensure photorealism and appearance alignment. LangDriveCTRL supports both object node editing (removal, insertion, and replacement) and multi-object behavior editing from natural-language instructions. Quantitatively, it achieves nearly $2\times$ higher instruction alignment than the previous SoTA, with superior photorealism, structural preservation, and traffic realism. Project page is available at: https://yunhe24.github.io/langdrivectrl/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces LangDriveCTRL, a framework for natural-language editing of real-world driving videos. Scenes are represented as explicit 3D scene graphs separating static background from dynamic object nodes. An Orchestrator agent decomposes instructions into executable graphs that coordinate specialized multi-modal agents: Object Grounding Agent for text-to-node alignment, Behavior Editing Agent for multi-object trajectory generation, Behavior Reviewer Agent for iterative refinement, and Video Reviewer Agent after video diffusion rendering. The system supports object insertion/removal/replacement and behavior editing. It reports nearly 2× higher instruction alignment than prior SoTA along with gains in photorealism, structural preservation, and traffic realism.

Significance. If the multi-agent pipeline proves reliable for accurate grounding, collision-free trajectories, and artifact-free rendering on complex real-world scenes, the work would provide a practical natural-language interface for synthesizing diverse driving scenarios, aiding data augmentation and simulation for autonomous driving. The explicit scene-graph representation and feedback-driven agents represent a coherent integration of language models with vision tools; the quantitative claim of doubled alignment would be a notable empirical advance if supported by rigorous controls.

major comments (3)
  1. [§5.1, Table 2] §5.1 and Table 2: the headline claim of nearly 2× higher instruction alignment is presented without ablations isolating the contribution of each agent (Orchestrator, Object Grounding, Behavior Editing, Reviewers) or per-agent success rates; without these, it is impossible to determine whether the reported gain stems from the proposed pipeline or from other factors, which is load-bearing for the central quantitative result.
  2. [§4.3] §4.3: the Behavior Editing Agent is stated to generate collision-free trajectories consistent with traffic rules, yet no quantitative metrics (collision rate, rule-violation count, or trajectory realism scores) are reported on dense/occluded nuScenes-style intersections; this omission leaves the traffic-realism superiority claim unsupported.
  3. [§5.2] §5.2: the Video Reviewer Agent is described as ensuring photorealism and 3D structure preservation after diffusion rendering, but the manuscript provides neither failure-case analysis nor metrics on introduced artifacts (e.g., ghosting, inconsistent lighting) across scene types; this is critical because the final output quality directly determines the photorealism and structural-preservation claims.
minor comments (2)
  1. [Figure 3] Figure 3: the pipeline diagram would be clearer if the feedback arrows between the Behavior Reviewer and Behavior Editing agents were labeled with the exact review criteria used.
  2. [§3.2] §3.2: the definition of the scene-graph node attributes (position, velocity, class) is introduced without an explicit equation; adding a compact notation would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the empirical support for our claims.

read point-by-point responses

  1. Referee: [§5.1, Table 2] §5.1 and Table 2: the headline claim of nearly 2× higher instruction alignment is presented without ablations isolating the contribution of each agent (Orchestrator, Object Grounding, Behavior Editing, Reviewers) or per-agent success rates; without these, it is impossible to determine whether the reported gain stems from the proposed pipeline or from other factors, which is load-bearing for the central quantitative result.

    Authors: We agree that component-wise ablations and per-agent success rates would strengthen the central quantitative claim. In the revised manuscript we will add a dedicated ablation study that systematically disables or replaces each agent (Orchestrator, Object Grounding Agent, Behavior Editing Agent, Behavior Reviewer, and Video Reviewer) while keeping the rest of the pipeline fixed, and we will report per-agent success rates on the instruction-alignment metric. These results will be included in an expanded §5.1 and updated Table 2. revision: yes

  2. Referee: [§4.3] §4.3: the Behavior Editing Agent is stated to generate collision-free trajectories consistent with traffic rules, yet no quantitative metrics (collision rate, rule-violation count, or trajectory realism scores) are reported on dense/occluded nuScenes-style intersections; this omission leaves the traffic-realism superiority claim unsupported.

    Authors: We acknowledge that quantitative metrics are needed to substantiate the traffic-realism claims for the Behavior Editing Agent. In the revision we will add collision-rate and rule-violation statistics evaluated on dense and occluded nuScenes intersections, together with trajectory-realism scores (e.g., against ground-truth trajectories). These metrics will be reported in an extended §4.3 and compared against baseline trajectory generators to support the superiority claim. revision: yes

  3. Referee: [§5.2] §5.2: the Video Reviewer Agent is described as ensuring photorealism and 3D structure preservation after diffusion rendering, but the manuscript provides neither failure-case analysis nor metrics on introduced artifacts (e.g., ghosting, inconsistent lighting) across scene types; this is critical because the final output quality directly determines the photorealism and structural-preservation claims.

    Authors: We agree that failure-case analysis and artifact metrics are important for validating the Video Reviewer Agent. The revised manuscript will include a new subsection in §5.2 that presents failure cases across scene types (e.g., urban, highway, occluded) and reports quantitative artifact scores for ghosting, lighting inconsistency, and structural drift. These additions will directly support the photorealism and structural-preservation claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes an empirical agentic pipeline for natural-language driving scene editing, built from an orchestrator, specialized multi-modal agents, scene-graph representation, and video diffusion rendering. No equations, fitted parameters, or first-principles derivations are presented that reduce by construction to their own inputs. Quantitative claims rest on direct comparisons to prior SoTA methods rather than self-referential definitions or load-bearing self-citations. The approach is self-contained as a new system description with external empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 5 invented entities

The framework introduces several new agent components and relies on the assumption that pre-trained multi-modal models can be orchestrated effectively for this task. No free parameters are explicitly fitted in the abstract description.

axioms (1)
  • domain assumption Driving scenes can be decomposed into static background and dynamic object nodes in a 3D scene graph
    Core representation used throughout the framework.
invented entities (5)
  • Orchestrator agent no independent evidence
    purpose: Converts user instructions into executable graphs coordinating agents
    New component introduced in the pipeline.
  • Object Grounding Agent no independent evidence
    purpose: Aligns text with object nodes
    Specialized agent for grounding.
  • Behavior Editing Agent no independent evidence
    purpose: Generates trajectories from language
    For behavior modification.
  • Behavior Reviewer Agent no independent evidence
    purpose: Reviews and refines trajectories
    Iterative refinement.
  • Video Reviewer Agent no independent evidence
    purpose: Ensures photorealism and alignment
    Final quality check.

pith-pipeline@v0.9.0 · 5529 in / 1553 out tokens · 27732 ms · 2026-05-16T20:53:29.941331+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages · cited by 1 Pith paper · 10 internal anchors

  1. [1]

    GPT-4 Technical Report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023. 2, 4, 6, 7, 13

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    World Simulation with Video Foundation Models for Physical AI

    Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji, Aaron Blakeman, Tiffany Cai, Jiaxin Cao, Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, et al. World simulation with video foundation models for physical ai.arXiv preprint arXiv:2511.00062, 2025. 1, 2, 3, 6, 7, 8, 13, 14, 16

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  3. [3]

    Qwen Technical Report

    Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report.arXiv preprint arXiv:2309.16609, 2023. 13

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  4. [4]

    Language models are few-shot learn- ers.Advances in neural information processing systems, 33:1877–1901, 2020

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learn- ers.Advances in neural information processing systems, 33:1877–1901, 2020. 4

    work page 1901

  5. [5]

    Emerging properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. InProceedings of the IEEE/CVF interna- tional conference on computer vision, pages 9650–9660,

    work page

  6. [6]

    Langtraj: Diffusion model and dataset for language-conditioned trajectory simulation.arXiv preprint arXiv:2504.11521,

    Wei-Jer Chang, Wei Zhan, Masayoshi Tomizuka, Man- mohan Chandraker, and Francesco Pittaluga. Langtraj: Diffusion model and dataset for language-conditioned trajectory simulation.arXiv preprint arXiv:2504.11521,

    work page arXiv

  7. [7]

    Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

    Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lu- tio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, et al. Om- nire: Omni urban scene reconstruction.arXiv preprint arXiv:2408.16760, 2024. 2, 3, 4, 13, 14

    work page arXiv 2024

  8. [8]

    arXiv preprint arXiv:2305.06558 (2023)

    Yangming Cheng, Liulei Li, Yuanyou Xu, Xiaodi Li, Zongxin Yang, Wenguan Wang, and Yi Yang. Segment and track anything.arXiv preprint arXiv:2305.06558,

    work page arXiv

  9. [9]

    Carla: An open ur- ban driving simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, An- tonio Lopez, and Vladlen Koltun. Carla: An open ur- ban driving simulator. InConference on robot learning, pages 1–16. PMLR, 2017. 1

    work page 2017

  10. [10]

    Density-based spatial clustering of applica- tions with noise

    Martin Ester, Hans-Peter Kriegel, J ¨org Sander, and Xi- aowei Xu. Density-based spatial clustering of applica- tions with noise. InInt. Conf. knowledge discovery and data mining, volume 240, 1996. 14

    work page 1996

  11. [11]

    Obbtree: A hierarchical structure for rapid interference detection

    Stefan Gottschalk, Ming C Lin, and Dinesh Manocha. Obbtree: A hierarchical structure for rapid interference detection. InProceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 171–180, 1996. 15

    work page 1996

  12. [12]

    Gem: A generalizable ego-vision multimodal world model for fine-grained ego-motion, object dynamics, and scene composition control

    Mariam Hassan, Sebastian Stapf, Ahmad Rahimi, Pedro Rezende, Yasaman Haghighi, David Br¨uggemann, Isinsu Katircioglu, Lin Zhang, Xiaoran Chen, Suman Saha, et al. Gem: A generalizable ego-vision multimodal world model for fine-grained ego-motion, object dynamics, and scene composition control. InProceedings of the Com- puter Vision and Pattern Recognition ...

    work page 2025

  13. [13]

    Kubrick: Multi- modal agent collaborations for synthetic video genera- tion.arXiv preprint arXiv:2408.10453, 2024

    Liu He, Yizhi Song, Hejun Huang, Pinxin Liu, Yunlong Tang, Daniel Aliaga, and Xin Zhou. Kubrick: Multi- modal agent collaborations for synthetic video genera- tion.arXiv preprint arXiv:2408.10453, 2024. 3

    work page arXiv 2024

  14. [14]

    Density-preserving deep point cloud compression

    Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, and Yanwei Fu. Density-preserving deep point cloud compression. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2333–2342, 2022. 3

    work page 2022

  15. [15]

    Grad-pu: Arbitrary-scale point cloud up- sampling via gradient descent with learned distance func- tions

    Yun He, Danhang Tang, Yinda Zhang, Xiangyang Xue, and Yanwei Fu. Grad-pu: Arbitrary-scale point cloud up- sampling via gradient descent with learned distance func- tions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5354– 5363, 2023. 3

    work page 2023

  16. [16]

    Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017. 6

    work page 2017

  17. [17]

    Autovfx: Physically realistic video editing from natural language instructions

    Hao-Yu Hsu, Chih-Hao Lin, Albert J Zhai, Hongchi Xia, and Shenlong Wang. Autovfx: Physically realistic video editing from natural language instructions. In2025 In- ternational Conference on 3D Vision (3DV), pages 769–

    work page

  18. [18]

    2, 3, 13, 14

    IEEE, 2025. 2, 3, 13, 14

    work page 2025

  19. [19]

    Scenecraft: An llm agent for synthesizing 3d scenes as blender code

    Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A Ross, Cordelia Schmid, and Alireza Fathi. Scenecraft: An llm agent for synthesizing 3d scenes as blender code. InForty-first International Conference on Machine Learning, 2024. 3 9

    work page 2024

  20. [20]

    GPT-4o System Card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Ak- ila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024. 4, 6, 7, 13

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  21. [21]

    3d gaussian splatting for real-time radiance field rendering.ACM Trans

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering.ACM Trans. Graph., 42(4):139–1, 2023. 2, 3, 4, 5, 13

    work page 2023

  22. [22]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. InProceedings of the IEEE/CVF international conference on computer vision, pages 4015–4026, 2023. 4, 12

    work page 2023

  23. [23]

    Five: A fine-grained video editing benchmark for evaluating emerging diffusion and rectified flow models.arXiv preprint arXiv:2503.13684, 2025

    Minghan Li, Chenxi Xie, Yichen Wu, Lei Zhang, and Mengyu Wang. Five: A fine-grained video editing benchmark for evaluating emerging diffusion and rec- tified flow models.arXiv preprint arXiv:2503.13684,

    work page arXiv

  24. [24]

    4d langsplat: 4d language gaussian splatting via multimodal large language models

    Wanhua Li, Renping Zhou, Jiawei Zhou, Yingwei Song, Johannes Herter, Minghan Qin, Gao Huang, and Hanspeter Pfister. 4d langsplat: 4d language gaussian splatting via multimodal large language models. InPro- ceedings of the Computer Vision and Pattern Recognition Conference, pages 22001–22011, 2025. 4, 12, 13

    work page 2025

  25. [25]

    Dif- fusion renderer: Neural inverse and forward rendering with video diffusion models

    Ruofan Liang, Zan Gojcic, Huan Ling, Jacob Munkberg, Jon Hasselgren, Chih-Hao Lin, Jun Gao, Alexander Keller, Nandita Vijaykumar, Sanja Fidler, et al. Dif- fusion renderer: Neural inverse and forward rendering with video diffusion models. InProceedings of the Com- puter Vision and Pattern Recognition Conference, pages 26069–26080, 2025. 3

    work page 2025

  26. [26]

    Driveeditor: A unified 3d information-guided framework for control- lable object editing in driving scenes

    Yiyuan Liang, Zhiying Yan, Liqun Chen, Jiahuan Zhou, Luxin Yan, Sheng Zhong, and Xu Zou. Driveeditor: A unified 3d information-guided framework for control- lable object editing in driving scenes. InProceedings of the AAAI Conference on Artificial Intelligence, vol- ume 39, pages 5164–5172, 2025. 2, 3, 13, 14

    work page 2025

  27. [27]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. InEuropean conference on computer vision, pages 38–

    work page

  28. [28]

    Springer, 2024. 4, 12

    work page 2024

  29. [29]

    Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99– 106, 2021

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99– 106, 2021. 3

    work page 2021

  30. [30]

    Simsplat: Predictive driv- ing scene editing with language-aligned 4d gaussian splatting.arXiv preprint arXiv:2510.02469, 2025

    Sung-Yeon Park, Adam Lee, Juanwu Lu, Can Cui, Luyang Jiang, Rohit Gupta, Kyungtae Han, Ahmadreza Moradipari, and Ziran Wang. Simsplat: Predictive driv- ing scene editing with language-aligned 4d gaussian splatting.arXiv preprint arXiv:2510.02469, 2025. 13

    work page arXiv 2025

  31. [31]

    Langsplat: 3d language gaus- sian splatting

    Minghan Qin, Wanhua Li, Jiawei Zhou, Haoqian Wang, and Hanspeter Pfister. Langsplat: 3d language gaus- sian splatting. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 20051–20060, 2024. 4

    work page 2024

  32. [32]

    Learning transferable visual models from natural lan- guage supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sas- try, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural lan- guage supervision. InInternational conference on ma- chine learning, pages 8748–8763. PmLR, 2021. 12

    work page 2021

  33. [33]

    Trace and pace: Controllable pedestrian animation via guided trajectory diffusion

    Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, and Or Litany. Trace and pace: Controllable pedestrian animation via guided trajectory diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13756–13766, 2023. 9

    work page 2023

  34. [34]

    Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

    Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kun- chang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al. Grounded sam: Assembling open- world models for diverse visual tasks.arXiv preprint arXiv:2401.14159, 2024. 4, 6, 12, 13, 15

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  35. [35]

    Airsim: High-fidelity visual and physical sim- ulation for autonomous vehicles

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical sim- ulation for autonomous vehicles. InField and service robotics: Results of the 11th international conference, pages 621–635. Springer, 2017. 1

    work page 2017

  36. [36]

    Language embedded 3d gaussians for open- vocabulary scene understanding

    Jin-Chuan Shi, Miao Wang, Hao-Bin Duan, and Shao- Hua Guan. Language embedded 3d gaussians for open- vocabulary scene understanding. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5333–5343, 2024. 4

    work page 2024

  37. [37]

    Denoising Diffusion Implicit Models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  38. [38]

    Synthetic datasets for autonomous driv- ing: A survey.IEEE Transactions on Intelligent Vehicles, 9(1):1847–1864, 2023

    Zhihang Song, Zimin He, Xingyu Li, Qiming Ma, Ruibo Ming, Zhiqi Mao, Huaxin Pei, Lihui Peng, Jianming Hu, Danya Yao, et al. Synthetic datasets for autonomous driv- ing: A survey.IEEE Transactions on Intelligent Vehicles, 9(1):1847–1864, 2023. 1

    work page 2023

  39. [39]

    Are Self-Driving Cars Closer Than We Think? Discover How Synthetic Data Is Paving the Way — spectrum.ieee.org.https://spectrum

    Eliza Strickland. Are Self-Driving Cars Closer Than We Think? Discover How Synthetic Data Is Paving the Way — spectrum.ieee.org.https://spectrum. ieee.org/synthetic- data- self- driving,

    work page

  40. [40]

    [Accessed 13-11-2025]. 1

    work page 2025

  41. [41]

    PyVista: 3D plotting and mesh analysis through a streamlined inter- face for the Visualization Toolkit (VTK).Journal of Open Source Software, 4(37):1450, May 2019

    Bane Sullivan and Alexander Kaszynski. PyVista: 3D plotting and mesh analysis through a streamlined inter- face for the Visualization Toolkit (VTK).Journal of Open Source Software, 4(37):1450, May 2019. 5

    work page 2019

  42. [42]

    Scal- ability in perception for autonomous driving: Waymo open dataset

    Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aure- lien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scal- ability in perception for autonomous driving: Waymo open dataset. InProceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pages 2446–2454, 2020. 6, 15

    work page 2020

  43. [43]

    Coma: Compositional human motion generation with multi-modal agents,

    Shanlin Sun, Gabriel De Araujo, Jiaqi Xu, Shenghan Zhou, Hanwen Zhang, Ziheng Huang, Chenyu You, and Xiaohui Xie. Coma: Compositional human mo- tion generation with multi-modal agents.arXiv preprint arXiv:2412.07320, 2024. 3 10

    work page arXiv 2024

  44. [44]

    Lidarf: Delv- ing into lidar for neural radiance field on street scenes

    Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, Buyu Liu, Xiaohui Xie, and Manmohan Chandraker. Lidarf: Delv- ing into lidar for neural radiance field on street scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19563–19572,

    work page

  45. [45]

    Block- nerf: Scalable large scene neural view synthesis

    Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block- nerf: Scalable large scene neural view synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8248–8258, 2022. 3

    work page 2022

  46. [46]

    Decentnerfs: Decentralized neural radiance fields from crowdsourced images

    Zaid Tasneem, Akshat Dave, Abhishek Singh, Kusha- gra Tiwary, Praneeth Vepakomma, Ashok Veeraragha- van, and Ramesh Raskar. Decentnerfs: Decentralized neural radiance fields from crowdsourced images. InEu- ropean Conference on Computer Vision, pages 144–161. Springer, 2024. 3

    work page 2024

  47. [47]

    LLaMA: Open and Efficient Foundation Language Models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Bap- tiste Rozi`ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023. 13

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  48. [48]

    Towards Accurate Generative Models of Video: A New Metric & Challenges

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Ku- rach, Raphael Marinier, Marcin Michalski, and Syl- vain Gelly. Towards accurate generative models of video: A new metric & challenges.arXiv preprint arXiv:1812.01717, 2018. 6

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  49. [49]

    Pacer+: On-demand pedestrian animation controller in driving scenarios

    Jingbo Wang, Zhengyi Luo, Ye Yuan, Yixuan Li, and Bo Dai. Pacer+: On-demand pedestrian animation controller in driving scenarios. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pages 718–728, 2024. 9

    work page 2024

  50. [50]

    Chatdyn: Language-driven multi-actor dynamics gener- ation in street scenes.arXiv preprint arXiv:2412.08685,

    Yuxi Wei, Jingbo Wang, Yuwen Du, Dingju Wang, Liang Pan, Chenxin Xu, Yao Feng, Bo Dai, and Siheng Chen. Chatdyn: Language-driven multi-actor dynamics gener- ation in street scenes.arXiv preprint arXiv:2412.08685,

    work page arXiv

  51. [51]

    Ed- itable scene simulation for autonomous driving via col- laborative llm-agents

    Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, and Yanfeng Wang. Ed- itable scene simulation for autonomous driving via col- laborative llm-agents. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pages 15077–15087, 2024. 1, 2, 3, 6, 7, 8, 13, 14, 16

    work page 2024

  52. [52]

    4d gaussian splatting for real-time dy- namic scene rendering

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xi- aopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xing- gang Wang. 4d gaussian splatting for real-time dy- namic scene rendering. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20310–20320, 2024. 12

    work page 2024

  53. [53]

    Difix3d+: Improving 3d re- constructions with single-step diffusion models

    Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xu- anchi Ren, Jun Gao, Mike Zheng Shou, Sanja Fidler, Zan Gojcic, and Huan Ling. Difix3d+: Improving 3d re- constructions with single-step diffusion models. InPro- ceedings of the Computer Vision and Pattern Recognition Conference, pages 26024–26035, 2025. 6

    work page 2025

  54. [54]

    Drivinggaussian++: To- wards realistic reconstruction and editable simulation for surrounding dynamic driving scenes.arXiv preprint arXiv:2508.20965, 2025

    Yajiao Xiong, Xiaoyu Zhou, Yongtao Wan, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian++: To- wards realistic reconstruction and editable simulation for surrounding dynamic driving scenes.arXiv preprint arXiv:2508.20965, 2025. 2, 3, 13, 14

    work page arXiv 2025

  55. [55]

    Wod-e2e: Waymo open dataset for end-to-end driving in challenging long-tail scenarios.arXiv preprint arXiv:2510.26125,

    Runsheng Xu, Hubert Lin, Wonseok Jeon, Hao Feng, Yuliang Zou, Liting Sun, John Gorman, Kate Tolstaya, Sarah Tang, Brandyn White, et al. Wod-e2e: Waymo open dataset for end-to-end driving in challenging long- tail scenarios.arXiv preprint arXiv:2510.26125, 2025. 1

    work page arXiv 2025

  56. [56]

    arXiv preprint arXiv:2304.11968 (2023)

    Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, and Feng Zheng. Track anything: Segment any- thing meets videos.arXiv preprint arXiv:2304.11968,

    work page arXiv

  57. [57]

    CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert trans- former.arXiv preprint arXiv:2408.06072, 2024. 6, 7

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  58. [58]

    Trajectorycrafter: Redirecting camera trajectory for monocular videos via diffusion models.arXiv preprint arXiv:2503.05638, 2025

    Mark YU, Wenbo Hu, Jinbo Xing, and Ying Shan. Trajectorycrafter: Redirecting camera trajectory for monocular videos via diffusion models.arXiv preprint arXiv:2503.05638, 2025. 7

    work page arXiv 2025

  59. [59]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. InProceedings of the IEEE/CVF international conference on computer vision, pages 3836–3847, 2023. 3

    work page 2023

  60. [60]

    Drivedreamer-2: Llm-enhanced world models for di- verse driving video generation

    Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, and Xingang Wang. Drivedreamer-2: Llm-enhanced world models for di- verse driving video generation. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 10412–10420, 2025. 3

    work page 2025

  61. [61]

    Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

    Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation.arXiv preprint arXiv:2501.12202,

    work page internal anchor Pith review Pith/arXiv arXiv

  62. [62]

    Parallel-r1: Towards parallel thinking via reinforcement learning.arXiv preprint arXiv:2509.07980, 2025a

    Tong Zheng, Hongming Zhang, Wenhao Yu, Xiaoyang Wang, Runpeng Dai, Rui Liu, Huiwen Bao, Chengsong Huang, Heng Huang, and Dong Yu. Parallel-r1: To- wards parallel thinking via reinforcement learning.arXiv preprint arXiv:2509.07980, 2025. 13

    work page arXiv 2025

  63. [63]

    Scenecrafter: Controllable multi-view driving scene editing

    Zehao Zhu, Yuliang Zou, Chiyu Max Jiang, Bo Sun, Vincent Casser, Xiukun Huang, Jiahao Wang, Zhenpei Yang, Ruiqi Gao, Leonidas Guibas, et al. Scenecrafter: Controllable multi-view driving scene editing. InPro- ceedings of the Computer Vision and Pattern Recognition Conference, pages 6812–6822, 2025. 2, 3, 13, 14 11 In the supplementary material, we provide...

    work page 2025

  64. [64]

    Ablation on Object Grounding Agent.For the open vocabulary object query task, we use Grounding SAM

    Additional Experiments In this section, we conduct more experiments to val- idate the effectiveness of ourObject Grounding Agent andBehavior Editing Agent. Ablation on Object Grounding Agent.For the open vocabulary object query task, we use Grounding SAM

    work page

  65. [65]

    The vehicle to the left of the red sedan

    and 4DLangSplat [23] as baselines. Grounding SAM [32] first performs open vocabulary detection on images through Grounding DINO [26] to obtain ob- ject bounding boxes. It then uses SAM [21] to gen- erate object masks based on these bounding boxes. 4DLangSplat [23] first reconstructs the dynamic scene through 4D Gaussian Splatting [49]. Each Gaussian primi...

    work page

  66. [66]

    Prior work can be roughly grouped into three categories

    Detailed Comparison with Related Work We provide a detailed comparison with previous driv- ing scene editing methods in Table 8. Prior work can be roughly grouped into three categories. The first category consists of diffusion-based meth- ods [2, 25, 60]. Among them, DriveEditor [25] and 1For Cosmos [2], we use Cosmos-Predict2.5 Base instead of Cosmos-Pre...

    work page

  67. [67]

    forgetting

    Implementation Details 8.1. Behavior Description Generation and Be- havior Validation Building upon [6], we extract semantic behavior de- scriptions from original object trajectory and introduce a novel automated engine for reasoning about the phys- ical and semantic consistency of counterfactual behav- iors. These technologies are leveraged by the Object...

    work page

  68. [68]

    8.1.4 Behavior Alignment Metric In Table 1, we calculate the behavior alignment metric using the same logic as in behavior description genera- tion

    is used to detect overlaps between vehicles for col- lision checking. 8.1.4 Behavior Alignment Metric In Table 1, we calculate the behavior alignment metric using the same logic as in behavior description genera- tion. Although our method generates explicit trajecto- ries during the editing process, we do not use them di- rectly for evaluation. Instead, t...

    work page 2005

  69. [69]

    Insert a green vehicle 3 meters to the right of the ego vehicle, slightly ahead, and make it change to the left lane

    Extra Qualitative Results In this section, we provide additional qualitative results. Specifically, Figure 7 shows editing results of different methods across various instruction types. As observed, Cosmos [2] modifies the original back- ground, while ChatSim [48] suffers from poor photo- realism. Moreover, neither method follows instructions well (e.g., ...

    work page

  70. [70]

    Failure Cases In this section, we present two common failure cases

    work page

  71. [71]

    For instance, the system may fail to properly recognize road separations such as median barriers, in- correctly treating them as drivable areas

    Generated trajectories sometimes still contain traffic violations. For instance, the system may fail to properly recognize road separations such as median barriers, in- correctly treating them as drivable areas. In Figure 10, the newly inserted vehicle drives on the median barrier

    work page

  72. [72]

    Insert a green vehicle 3 meters to the right of the ego vehicle, slightly ahead, and make it change to the left lane

    The video diffusion model (VDM) sometimes alters the type and color of inserted vehicles. For example, in Figure 11, while a green convertible mesh is inserted, it becomes a black sedan after refinement. This occurs because the VDM was trained primarily on common ve- hicle types (e.g., sedans and SUVs) and colors, resulting in poor handling of uncommon ve...

    work page

  73. [73]

    @@- Removing object

    Object Manipulation: Remove object: logging.info("@@- Removing object") remove object(...) Add new object: target obj = retrieve from hunyuan(...) # IMPORTANT: Rescale and transform the generated mesh: target obj = rescale and transform mesh(...) Replace with new object: logging.info("@@- Replacing with new object") new obj = replace object(...)

    work page

  74. [74]

    Trajectory/Behavior Generation: generate counterfactual behavior(...) generate trajectory(...) review and refine trajectories(...)

    work page

  75. [75]

    Add a red sports car to the right of the yellow car and make it turn right

    Camera Operations: 23 translate camera(...) rotate camera(...) Example: Input: “Add a red sports car to the right of the yellow car and make it turn right.” Output: Template A + Core Editing + Template B + Template C Core Editing Operation: logging.info("@@- Adding the new generated vehicle") target obj = retrieve from hunyuan(...) logging.info("@@@@• Ali...

    work page

  76. [76]

    Decompose the description into structured triplets

    work page

  77. [77]

    Identify the reference object and filter candidates by direction

    work page

  78. [78]

    Match attributes to find the target object

    work page

  79. [79]

    IMPORTANT RULES:

    Return the ID(s) of matching object(s) Step 1: Triplet Decomposition Extract natural-language descriptions of EXISTING objects that need ID conversion from the instruction. IMPORTANT RULES:

    work page

  80. [80]

    car 2”, “vehicle id 5

    IGNORE descriptions that already specify an ID (like “car 2”, “vehicle id 5”) - leave them unchanged in the final instruction

    work page

Showing first 80 references.