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arxiv: 2606.03994 · v1 · pith:2X6FICVBnew · submitted 2026-06-02 · 💻 cs.CV · cs.RO

SimuScene: Simulation-Ready Compositional 3D Scene Reconstruction from a Single Image

Inhee Lee , Sangwon Baik , Sungjoo Kim , Hyeonwoo Kim , Hyunsoo Cha , Hanbyul Joo This is my paper

Pith reviewed 2026-06-28 10:56 UTC · model grok-4.3

classification 💻 cs.CV cs.RO
keywords 3D scene reconstructionsingle imagephysics simulationcompositional reconstructionsimulation-ready scenesgravity feedbackshape correctionrobotic manipulation
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The pith

A physics engine run during single-image 3D reconstruction corrects object shapes and layouts so the resulting scene stays stable under simulation.

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

The paper establishes that single-image 3D reconstruction methods produce per-object shapes whose errors cause scenes to interpenetrate, hover, or sink when placed in a physics simulator. It shows that feeding the outcomes of gravity simulation back into the estimation process supplies quantitative signals for stretching objects along the gravity axis and resampling their amodal shapes. This closed loop reduces the accumulated geometric errors that arise from composing independent lifts and yields scenes that remain intact under physical interaction. The authors demonstrate the approach on stability and alignment benchmarks and show it enables direct transfer to humanoid control and robot-arm tasks.

Core claim

SimuScene places a physics engine inside the generative reconstruction pipeline itself. Diagnostic simulation of candidate objects under gravity converts observed penetration and support failures into correction signals that drive gravity-axis stretching and amodal shape resampling, thereby mitigating reconstruction errors and producing stable, simulation-ready compositional scenes.

What carries the argument

The physics-informed feedback loop that turns simulation failures into quantitative correction signals for shape and layout adjustment.

If this is right

  • Reconstructed scenes remain stable under gravity-driven physical simulation without collapse or interpenetration.
  • The same pipeline improves both physical stability metrics and geometric alignment to the input image.
  • Reconstructed environments transfer directly to downstream control tasks such as humanoid locomotion and robot-arm manipulation.
  • Physics simulation serves as an active diagnostic rather than a post-hoc cleanup step.

Where Pith is reading between the lines

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

  • The same feedback mechanism could be applied to video or multi-view inputs whose initial lifts already contain more geometric constraints.
  • Extending the loop to additional physical constraints such as friction or contact forces would address failure modes beyond gravity alone.
  • The approach may reduce the need for large-scale supervised 3D datasets if the physics signals supply sufficient self-supervision.

Load-bearing premise

Initial per-object shapes recovered from the single image are sufficiently close to physical plausibility that running a physics engine on them produces usable correction signals rather than divergence.

What would settle it

A dataset of single-image scenes where the corrected outputs still exhibit interpenetrations, hovering, or sinking when placed in an independent physics simulator would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.03994 by Hanbyul Joo, Hyeonwoo Kim, Hyunsoo Cha, Inhee Lee, Sangwon Baik, Sungjoo Kim.

Figure 1
Figure 1. Figure 1: SimuScene reconstructs simulation-ready compositional 3D scenes from a single image by using physics simulation to correct object shape and pose. Compared with conventional 3D lifters such as SAM3D [12], our method handles heavy occlusion and produces stable layouts. Red boxes highlight physics-guided shape corrections. Abstract Reconstructing interactive, simulation-ready 3D scenes from a single image is … view at source ↗
Figure 2
Figure 2. Figure 2: Pipeline Overview. (a) From a single image I, foundation priors decompose the scene into object meshes with poses. (b) Each object then passes through pose initialization, diagnostic physics simulation, and shape correction; completed objects are frozen as colliders, and physics displacements drive the correction. The corrected objects settle into a simulation-ready 3D scene. editor [50] and run SAM3D [12]… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Comparison of GenWild. All scenes are visualized after gravity-driven physics simulation. Baselines either remain artificially stuck mid-air due to interpenetration or collapse catastrophically, while our method preserves both physical plausibility and input-view alignment in cluttered scenes. GraspClutter6D [5] AriaDigitalTwin [42] GenWild Method ABOfh/fo ↑ ABO ↑ Dmean ↓ Epk ↓ Penetr. ↓ 3D ABO… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Ablation. Post-simulation rendering of 3D scene. From SAM3D, we progressively add per-object alignment (Align.), penetration resolution (Pen.), stabilized gravity simula￾tion (Grv.), and shape resampling (Re.). The red arrows indicate that resampling successfully generates plausible, aligned shapes. Variants ABO ↑ ABOfh/fo ↑ Dmean ↓ SAM3D 0.263 0.218 0.555 +Align. 0.409 0.361 0.364 +Pen. 0.403 … view at source ↗
Figure 5
Figure 5. Figure 5: Applications. Two downstream uses of our object-complete physics-stable reconstructions: (a) physics-based character control [29], (b) cluttered robotic manipulation [6]. 3D-RE-GEN, we replace its background mesh with our wall boundary while retaining its native floor parameters; despite this setup, its objects still fail to settle stably and maintain high energy errors. Qualitative Results. Visual compari… view at source ↗
Figure 6
Figure 6. Figure 6: Distinct Instance Extraction and Decluttered Image Ibase Generation. (a) We generate a decluttered image Ibase via MLLM [50] and compute an object probability map from pixel differences with the original image. Candidate masks in the original image are filtered by their overlap with this map to identify liftable instances, while masks in the decluttered image are selected using Set-of￾Mark-based VLM filter… view at source ↗
Figure 7
Figure 7. Figure 7: VLM-based wall-hang detection. Each object is rendered into a two-panel query image — highlighted overlay (left) and color-on-grayscale isolation (right) — and classified by a VLM as standing or hanging under an own-attachment-only rule, with hanging objects additionally tagged with a pin count (0: free, 1: point-anchored, 2: line-anchored). a pair: a point p (k) o on the object’s surface and its mate p (k… view at source ↗
Figure 8
Figure 8. Figure 8: SAM3D Fine-tuning Pipeline We fine-tune SAM3D for amodal shape resampling using synthetic preference pairs that contrast occlusion-failed latents with completed amodal latents. A flow-matching DPO objective updates only LoRA adapters while keeping the reference SAM3D frozen, and the resulting model is used at test time for physics-informed resampling. dynamic body. At every iteration we read the resulting … view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative Comparison of ADT. Additional qualitative comparisons on the ADT dataset. and Stab., plus 3D IoU on ADT. For Gen3DSR and 3D-RE-GEN we also evaluate a variant that swaps in our background reconstruction (“our bg”) to isolate the foreground predictor from the layout. Tab. 4 shows that Ours sets the new state of the art across all three datasets, leading on identity-paired IoU, ADT 3D IoU, and mos… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative Comparison of GraspClutter6D. Additional qualitative comparisons on the GraspClutter6D dataset. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
read the original abstract

Reconstructing interactive, simulation-ready 3D scenes from a single image is a critical bottleneck for robotic manipulation. While recent single-image lifters recover plausible per-object shapes, composing them yields scenes that collapse under physical simulation due to interpenetrating, hovering, or sinking objects. Existing physics-aware methods address this strictly as a post-hoc layout correction, leaving the underlying geometric errors unresolved. To address this, we introduce SimuScene, a compositional 3D reconstruction pipeline that puts physics in the loop of shape and layout estimation. Rather than using physics merely for layout cleanup, we utilize the physics engine as a diagnostic measurement tool during the generative process itself. By diagnostically simulating reconstructed objects under gravity, we convert penetration and support failures into quantitative correction signals that drive gravity-axis stretching and amodal shape resampling. This physics-informed feedback loop mitigates accumulated reconstruction errors and produces a stable, simulation-ready compositional 3D scene. Extensive experiments demonstrate state-of-the-art performance on physical stability and geometric alignment benchmarks. We further highlight SimuScene's utility by deploying reconstructed environments in humanoid control and robot-arm manipulation tasks.

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

2 major / 3 minor

Summary. The manuscript introduces SimuScene, a compositional 3D reconstruction pipeline from a single image that incorporates physics simulation directly into shape and layout estimation. Diagnostic simulations under gravity convert penetration and support failures into correction signals that drive gravity-axis stretching and amodal shape resampling, producing stable, simulation-ready scenes. The work reports state-of-the-art results on physical stability and geometric alignment benchmarks and demonstrates utility in humanoid control and robot-arm manipulation tasks.

Significance. If the central claims hold, the work provides a substantive advance over post-hoc physics correction methods by closing the loop with physics-derived signals during generation. This addresses a practical bottleneck for robotic applications and simulation. The explicit use of an external physics engine for diagnostic feedback, combined with reported empirical validation on benchmarks and downstream tasks, strengthens the contribution. The approach yields falsifiable predictions about scene stability that can be directly tested in simulation.

major comments (2)
  1. [§4] §4 (Method), feedback loop description: the claim that diagnostic simulation supplies quantitative correction signals for gravity-axis stretching assumes the initial per-object geometry is close enough to physical plausibility for the signals to be informative; the manuscript should include a quantitative analysis or failure-case study showing the basin of convergence for this assumption, as it is load-bearing for the central claim that accumulated reconstruction errors are mitigated.
  2. [Table 2] Table 2 (physical stability benchmark): the reported SOTA margins on penetration and support metrics must be accompanied by per-scene breakdowns and variance across the test set; without this, it is unclear whether the physics-in-the-loop corrections consistently outperform post-hoc baselines or only succeed on easier subsets.
minor comments (3)
  1. [§3.1] §3.1: the notation for amodal shape resampling is introduced without an explicit equation relating the resampling distribution to the physics-derived support signal; adding this would improve reproducibility.
  2. [Figure 4] Figure 4: the before/after simulation visualizations would benefit from overlaid penetration depth heatmaps to make the quantitative correction signals visually verifiable.
  3. [Related Work] Related work section: the distinction between SimuScene and prior physics-aware layout methods could be sharpened by directly comparing the timing of physics usage (during vs. after generation) in a table.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We respond to each major comment below.

read point-by-point responses

  1. Referee: [§4] §4 (Method), feedback loop description: the claim that diagnostic simulation supplies quantitative correction signals for gravity-axis stretching assumes the initial per-object geometry is close enough to physical plausibility for the signals to be informative; the manuscript should include a quantitative analysis or failure-case study showing the basin of convergence for this assumption, as it is load-bearing for the central claim that accumulated reconstruction errors are mitigated.

    Authors: We agree that the basin of convergence for the diagnostic feedback loop is a load-bearing assumption and that the current manuscript does not contain a quantitative analysis or dedicated failure-case study of this property. We will add such an analysis, including performance under controlled increases in initial geometric error, to the revised version. revision: yes

  2. Referee: [Table 2] Table 2 (physical stability benchmark): the reported SOTA margins on penetration and support metrics must be accompanied by per-scene breakdowns and variance across the test set; without this, it is unclear whether the physics-in-the-loop corrections consistently outperform post-hoc baselines or only succeed on easier subsets.

    Authors: We agree that aggregate SOTA margins alone leave open the question of consistency across scenes. We will revise Table 2 to include per-scene results together with variance or standard deviation statistics over the test set. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and high-level description present a feedback loop that applies an independent external physics engine to diagnose and correct per-object geometry recovered from a single image. No equations, fitted parameters, or self-citations are shown that would reduce the claimed correction signals (gravity-axis stretching, amodal resampling) to redefinitions of the input reconstructions. The method treats the physics simulator as an external diagnostic oracle whose outputs are not constructed from the reconstruction model itself; convergence depends on an explicit assumption about initial geometry quality rather than any definitional equivalence. This is a standard non-circular engineering pipeline.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only input supplies no explicit free parameters, axioms, or invented entities; full manuscript required for ledger construction.

pith-pipeline@v0.9.1-grok · 5749 in / 1028 out tokens · 31850 ms · 2026-06-28T10:56:57.181251+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

65 extracted references · 15 linked inside Pith

  1. [1]

    Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023

    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. 4, 19

    Pith/arXiv arXiv 2023

  2. [2]

    Seeing through clutter: Structured 3d scene reconstruction via iterative object removal

    Rio Aguina-Kang, Kevin James Blackburn-Matzen, Thibault Groueix, Vladimir Kim, and Matheus Gadelha. Seeing through clutter: Structured 3d scene reconstruction via iterative object removal. In3DV, 2026. 3

    2026

  3. [3]

    Gen3dsr: Generalizable 3d scene reconstruction via divide and conquer from a single view

    Andreea Ardelean, Mert Özer, and Bernhard Egger. Gen3dsr: Generalizable 3d scene reconstruction via divide and conquer from a single view. In3DV, 2025. 7, 8, 14, 21

    2025

  4. [4]

    A general theoretical paradigm to understand learning from human preferences

    Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. A general theoretical paradigm to understand learning from human preferences. InAISTATS, 2024. 3

    2024

  5. [5]

    Graspclutter6d: A large-scale real-world dataset for robust perception and grasping in cluttered scenes.RA-L, 2025

    Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, et al. Graspclutter6d: A large-scale real-world dataset for robust perception and grasping in cluttered scenes.RA-L, 2025. 7, 14, 21, 22

    2025

  6. [6]

    Text-guided 6d object pose rearrangement via closed-loop vlm agents.arXiv:2604.09781, 2026

    Sangwon Baik, Gunhee Kim, Mingi Choi, and Hanbyul Joo. Text-guided 6d object pose rearrangement via closed-loop vlm agents.arXiv:2604.09781, 2026. 9, 21

    Pith/arXiv arXiv 2026

  7. [7]

    Training diffusion models with reinforcement learning.arXiv preprint arXiv:2305.13301, 2023

    Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning.arXiv preprint arXiv:2305.13301, 2023. 3

    Pith/arXiv arXiv 2023

  8. [8]

    Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023. 2

    Pith/arXiv arXiv 2023

  9. [9]

    Sam 3: Segment anything with concepts.arXiv preprint arXiv:2511.16719, 2025

    Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, et al. Sam 3: Segment anything with concepts.arXiv preprint arXiv:2511.16719, 2025. 4

    Pith/arXiv arXiv 2025

  10. [10]

    Physgen3d: Crafting a miniature interactive world from a single image

    Boyuan Chen, Hanxiao Jiang, Shaowei Liu, Saurabh Gupta, Yunzhu Li, Hao Zhao, and Shenlong Wang. Physgen3d: Crafting a miniature interactive world from a single image. InCVPR, 2025. 3

    2025

  11. [11]

    Videocrafter2: Overcoming data limitations for high-quality video diffusion models

    Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. InCVPR, 2024. 2

    2024

  12. [12]

    Sam 3d: 3dfy anything in images.arXiv preprint arXiv:2511.16624,

    Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, et al. Sam 3d: 3dfy anything in images.arXiv preprint arXiv:2511.16624,

    Pith/arXiv arXiv

  13. [13]

    1, 2, 3, 4, 7, 8, 14, 21

  14. [14]

    Luciddreamer: Domain-free generation of 3d gaussian splatting scenes.TVCG, 2025

    Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes.TVCG, 2025. 2

    2025

  15. [15]

    Hiscene: creating hierarchical 3d scenes with isometric view generation

    Wenqi Dong, Bangbang Yang, Zesong Yang, Yuan Li, Tao Hu, Hujun Bao, Yuewen Ma, and Zhaopeng Cui. Hiscene: creating hierarchical 3d scenes with isometric view generation. InACMMM, 2025. 3

    2025

  16. [16]

    Kto: Model alignment as prospect theoretic optimization.arXiv preprint arXiv:2402.01306, 2024

    Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. Kto: Model alignment as prospect theoretic optimization.arXiv preprint arXiv:2402.01306, 2024. 3

    Pith/arXiv arXiv 2024

  17. [17]

    Graspnet-1billion: A large-scale benchmark for general object grasping

    Hao-Shu Fang, Chenxi Wang, Minghao Gou, and Cewu Lu. Graspnet-1billion: A large-scale benchmark for general object grasping. InICCV, 2020. 9, 21 10

    2020

  18. [18]

    Cat3d: Create anything in 3d with multi-view diffusion models

    Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srini- vasan, Jonathan T Barron, and Ben Poole. Cat3d: Create anything in 3d with multi-view diffusion models. arXiv preprint arXiv:2405.10314, 2024. 2

    Pith/arXiv arXiv 2024

  19. [19]

    Mediapipe solutions guide

    Google AI Edge. Mediapipe solutions guide. https://developers.google.com/mediapipe, 2025. 21

    2025

  20. [20]

    Reparo: Compositional 3d assets generation with differentiable 3d layout alignment

    Haonan Han, Rui Yang, Huan Liao, Jiankai Xing, Zunnan Xu, Xiaoming Yu, Junwei Zha, Xiu Li, and Wanhua Li. Reparo: Compositional 3d assets generation with differentiable 3d layout alignment. InICCV,

  21. [21]

    Streamingt2v: Consistent, dynamic, and extendable long video generation from text

    Roberto Henschel, Levon Khachatryan, Hayk Poghosyan, Daniil Hayrapetyan, Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Streamingt2v: Consistent, dynamic, and extendable long video generation from text. InCVPR, 2025. 2

    2025

  22. [22]

    Video diffusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. InNeurIPS, 2022. 2

    2022

  23. [23]

    Orpo: Monolithic preference optimization without reference model

    Jiwoo Hong, Noah Lee, and James Thorne. Orpo: Monolithic preference optimization without reference model. InEMNLP, 2024. 3

    2024

  24. [24]

    LoRA: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. InICLR, 2022. 6, 7, 19

    2022

  25. [25]

    Flash sculptor: Modular 3d worlds from objects

    Yujia Hu, Songhua Liu, Xingyi Yang, and Xinchao Wang. Flash sculptor: Modular 3d worlds from objects. arXiv preprint arXiv:2504.06178, 2025. 3

    arXiv 2025

  26. [26]

    Holistic 3d scene parsing and reconstruction from a single rgb image

    Siyuan Huang, Siyuan Qi, Yixin Zhu, Yinxue Xiao, Yuanlu Xu, and Song-Chun Zhu. Holistic 3d scene parsing and reconstruction from a single rgb image. InECCV, 2018. 3

    2018

  27. [27]

    Open-set image tagging with multi-grained text supervision

    Xinyu Huang, Yi-Jie Huang, Youcai Zhang, Weiwei Tian, Rui Feng, Yuejie Zhang, Yanchun Xie, Yaqian Li, and Lei Zhang. Open-set image tagging with multi-grained text supervision. InMM, 2025. 4

    2025

  28. [28]

    Midi: Multi-instance diffusion for single image to 3d scene generation

    Zehuan Huang, Yuan-Chen Guo, Xingqiao An, Yunhan Yang, Yangguang Li, Zi-Xin Zou, Ding Liang, Xihui Liu, Yan-Pei Cao, and Lu Sheng. Midi: Multi-instance diffusion for single image to 3d scene generation. InCVPR, 2025. 2, 3

    2025

  29. [29]

    3d gaussian splatting for real-time radiance field rendering.TOG, 42(4):139–1, 2023

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis, et al. 3d gaussian splatting for real-time radiance field rendering.TOG, 42(4):139–1, 2023. 2

    2023

  30. [30]

    Devi: Physics-based dexterous human-object interaction via synthetic video imitation

    Hyeonwoo Kim, Jeonghwan Kim, Kyungwon Cho, and Hanbyul Joo. Devi: Physics-based dexterous human-object interaction via synthetic video imitation. InarXiv:2604.20841, 2026. 9

    Pith/arXiv arXiv 2026

  31. [31]

    FLUX.2: Frontier Visual Intelligence.https://bfl.ai/blog/flux-2, 2025

    Black Forest Labs. FLUX.2: Frontier Visual Intelligence.https://bfl.ai/blog/flux-2, 2025. 19

    2025

  32. [32]

    Phip-g: Physics-guided text-to-3d compositional scene generation.arXiv preprint arXiv:2502.00708, 2025

    Qixuan Li, Chao Wang, Zongjin He, and Yan Peng. Phip-g: Physics-guided text-to-3d compositional scene generation.arXiv preprint arXiv:2502.00708, 2025. 3

    arXiv 2025

  33. [33]

    Dso: Aligning 3d generators with simulation feedback for physical soundness

    Ruining Li, Chuanxia Zheng, Christian Rupprecht, and Andrea Vedaldi. Dso: Aligning 3d generators with simulation feedback for physical soundness. InICCV, 2025. 2, 3, 6

    2025

  34. [34]

    Aesthetic post-training diffusion models from generic preferences with step-by-step preference optimization

    Zhanhao Liang, Yuhui Yuan, Shuyang Gu, Bohan Chen, Tiankai Hang, Mingxi Cheng, Ji Li, and Liang Zheng. Aesthetic post-training diffusion models from generic preferences with step-by-step preference optimization. InCVPR, 2025. 3

    2025

  35. [35]

    Pat3d: Physics-augmented text-to-3d scene generation.arXiv preprint arXiv:2511.21978, 2025

    Guying Lin, Kemeng Huang, Michael Liu, Ruihan Gao, Hanke Chen, Lyuhao Chen, Beijia Lu, Taku Komura, Yuan Liu, Jun-Yan Zhu, et al. Pat3d: Physics-augmented text-to-3d scene generation.arXiv preprint arXiv:2511.21978, 2025. 3

    Pith/arXiv arXiv 2025

  36. [36]

    Scenethesis: A language and vision agentic framework for 3d scene generation.arXiv preprint arXiv:2505.02836, 2025

    Lu Ling, Chen-Hsuan Lin, Tsung-Yi Lin, Yifan Ding, Yu Zeng, Yichen Sheng, Yunhao Ge, Ming-Yu Liu, Aniket Bera, and Zhaoshuo Li. Scenethesis: A language and vision agentic framework for 3d scene generation.arXiv preprint arXiv:2505.02836, 2025. 3

    arXiv 2025

  37. [37]

    Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. InICLR, 2023. 6

    2023

  38. [38]

    Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017. 7 11

    Pith/arXiv arXiv 2017

  39. [39]

    Simpo: Simple preference optimization with a reference-free reward.NeurIPS, 2024

    Yu Meng, Mengzhou Xia, and Danqi Chen. Simpo: Simple preference optimization with a reference-free reward.NeurIPS, 2024. 3

    2024

  40. [40]

    Scenegen: Single-image 3d scene generation in one feedforward pass

    Yanxu Meng, Haoning Wu, Ya Zhang, and Weidi Xie. Scenegen: Single-image 3d scene generation in one feedforward pass. In3DV, 2026. 2

    2026

  41. [41]

    Phyrecon: Physically plausible neural scene reconstruction.NeurIPS, 2024

    Junfeng Ni, Yixin Chen, Bohan Jing, Nan Jiang, Bin Wang, Bo Dai, Puhao Li, Yixin Zhu, Song-Chun Zhu, and Siyuan Huang. Phyrecon: Physically plausible neural scene reconstruction.NeurIPS, 2024. 3

    2024

  42. [42]

    Training language models to follow instructions with human feedback.NeurIPS, 2022

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback.NeurIPS, 2022. 3

    2022

  43. [43]

    Aria digital twin: A new benchmark dataset for egocentric 3d machine perception

    Xiaqing Pan, Nicholas Charron, Yongqian Yang, Scott Peters, Thomas Whelan, Chen Kong, Omkar Parkhi, Richard Newcombe, and Yuheng Carl Ren. Aria digital twin: A new benchmark dataset for egocentric 3d machine perception. InICCV, 2023. 7, 8, 14, 21, 22

    2023

  44. [44]

    Corenet: Coherent 3d scene reconstruction from a single rgb image

    Stefan Popov, Pablo Bauszat, and Vittorio Ferrari. Corenet: Coherent 3d scene reconstruction from a single rgb image. InECCV, 2020. 3

    2020

  45. [45]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. InNeurIPS, 2023. 3, 6

    2023

  46. [46]

    Gen3c: 3d-informed world-consistent video generation with precise camera control

    Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3c: 3d-informed world-consistent video generation with precise camera control. InCVPR, 2025. 2

    2025

  47. [47]

    3d-re-gen: 3d reconstruction of indoor scenes with a generative framework.arXiv preprint arXiv:2512.17459, 2025

    Tobias Sautter, Jan-Niklas Dihlmann, and Hendrik Lensch. 3d-re-gen: 3d reconstruction of indoor scenes with a generative framework.arXiv preprint arXiv:2512.17459, 2025. 7, 8, 14, 21

    arXiv 2025

  48. [48]

    Dinov3.arXiv preprint arXiv:2508.10104, 2025

    Oriane Siméoni, Huy V V o, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, et al. Dinov3.arXiv preprint arXiv:2508.10104, 2025. 15

    Pith/arXiv arXiv 2025

  49. [49]

    Make-a-video: Text-to-video generation without text-video data

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. InICLR,

  50. [50]

    The open motion planning library.RAM, 19(4):72–82,

    Ioan A Sucan, Mark Moll, and Lydia E Kavraki. The open motion planning library.RAM, 19(4):72–82,

  51. [51]

    Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023. 4, 8, 14

    Pith/arXiv arXiv 2023

  52. [52]

    Mujoco: A physics engine for model-based control

    Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. 7, 8

    2012

  53. [53]

    Diffusion model alignment using direct preference optimization

    Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. InCVPR, 2024. 3, 6

    2024

  54. [54]

    Moge-2: Accurate monocular geometry with metric scale and sharp details

    Ruicheng Wang, Sicheng Xu, Yue Dong, Yu Deng, Jianfeng Xiang, Zelong Lv, Guangzhong Sun, Xin Tong, and Jiaolong Yang. Moge-2: Accurate monocular geometry with metric scale and sharp details. arXiv preprint arXiv:2507.02546, 2025. 4

    Pith/arXiv arXiv 2025

  55. [55]

    Foundationpose: Unified 6d pose estimation and tracking of novel objects

    Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. InCVPR, 2024. 4, 15

    2024

  56. [56]

    Gpt-4v(ision) is a human-aligned evaluator for text-to-3d generation

    Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v(ision) is a human-aligned evaluator for text-to-3d generation. InCVPR, 2024. 8, 9

    2024

  57. [57]

    Simrecon: Simready compositional scene reconstruction from real videos, 2026

    Chong Xia, Kai Zhu, Zizhuo Wang, Fangfu Liu, Zhizheng Zhang, and Yueqi Duan. Simrecon: Simready compositional scene reconstruction from real videos, 2026. 3

    2026

  58. [58]

    Physic: Physically plausible 3d human-scene interaction and contact from a single image

    Pradyumna Yalandur Muralidhar, Yuxuan Xue, Xianghui Xie, Margaret Kostyrko, and Gerard Pons-Moll. Physic: Physically plausible 3d human-scene interaction and contact from a single image. InACM SIGGRAPH Asia, 2025. 3 12

    2025

  59. [59]

    Phycage: Physically plausible compositional 3d asset generation from a single image.arXiv preprint arXiv:2411.18548, 2024

    Han Yan, Mingrui Zhang, Yang Li, Chao Ma, and Pan Ji. Phycage: Physically plausible compositional 3d asset generation from a single image.arXiv preprint arXiv:2411.18548, 2024. 3

    arXiv 2024

  60. [60]

    Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v.arXiv preprint arXiv:2310.11441, 2023

    Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v.arXiv preprint arXiv:2310.11441, 2023. 14

    Pith/arXiv arXiv 2023

  61. [61]

    Using human feedback to fine-tune diffusion models without any reward model

    Kai Yang, Jian Tao, Jiafei Lyu, Chunjiang Ge, Jiaxin Chen, Weihan Shen, Xiaolong Zhu, and Xiu Li. Using human feedback to fine-tune diffusion models without any reward model. InCVPR, 2024. 3

    2024

  62. [62]

    Cast: Component-aligned 3d scene reconstruction from an rgb image

    Kaixin Yao, Longwen Zhang, Xinhao Yan, Yan Zeng, Qixuan Zhang, Wei Yang, Lan Xu, Jiayuan Gu, and Jingyi Yu. Cast: Component-aligned 3d scene reconstruction from an rgb image. InSIGGRAPH, 2025. 2, 3

    2025

  63. [63]

    Freeman, and Jiajun Wu

    Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T. Freeman, and Jiajun Wu. Wonderworld: Interactive 3d scene generation from a single image. InCVPR, 2025. 2

    2025

  64. [64]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.TPAMI, 2025

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.TPAMI, 2025. 2

    2025

  65. [65]

    standing

    Jensen Zhou, Hang Gao, Vikram V oleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. InICCV, 2025. 2 13 Input Image Decluttered ImageDINO features Movable-Only Mask SAM3 + Merging Base-Only Mask Filtering MLLM Similarity & Mask (b) Ba...

    2025