Pith. sign in

REVIEW 3 major objections 5 minor 11 cited by

LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that pairing a vision-language model's pose guesses with differentiable spatial-relation constraints yields 3D layouts that are both physically plausible and semantically faithful, improving the PSA score by 40.8 over the…

desk verdict LayoutVLM is a genuinely new combination of VLM-predict poses and differentiable spatial relations, but the headline PSA gain is mostly a physical-plausibility artifact rather than evidence of better semantic alignment. read the letter →

arxiv 2412.02193 v3 pith:S3YU4SGR submitted 2024-12-03 cs.CV cs.AI

Fan-Yun Sun , Weiyu Liu , Siyi Gu , Dylan Lim , Goutam Bhat , Federico Tombari , Manling Li , Nick Haber
show 1 more author
Jiajun Wu
This is my paper · ORCID
classification cs.CVcs.AI
keywords 3Dlayoutgenerationvision-languagemodelsdifferentiableoptimizationspatialrelationsself-consistentdecodingscenerepresentationphysicalplausibilitysemanticalignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that open-universe 3D layout generation — arranging arbitrary unlabeled assets in a room according to free-form language instructions — can simultaneously satisfy physical plausibility and semantic intent. It argues that prior LLM-based methods either produce collisions and out-of-bound placements or fail on dense scenes, and that a vision-language model can do better if its numerical pose guesses are combined with differentiable spatial-relation constraints. The authors introduce LayoutVLM, which lets a VLM generate both initial object poses and a set of spatial relations, then optimizes the poses so that the final layout is collision-free and inside the room while preserving the instructed semantics. They report a 40.8-point improvement in the Physically-Grounded Semantic Alignment Score over the best baseline, I-Design, averaged across 11 room types, and they show that fine-tuning open-source VLMs on their scene layout representation improves those models' layout reasoning.

What carries the argument

The central object is the scene layout representation: a list of initial 3D poses plus a set of five differentiable spatial relations — distance, on_top_of, align_with, point_towards, and against_wall — each defined as a cost function on object poses. The VLM generates both parts from rendered, visually marked images (coordinate grids, front-facing arrows, and asset views); self-consistent decoding filters the relations by checking which ones the initial poses already satisfy; and projected gradient descent optimizes the poses against these relations plus a Distance-IoU collision loss, projecting assets back inside the room boundary during optimization. This lets the initial semantic guesses be refined into physically valid placements without a separate constraint solver.

What would settle it

Take the 33 test rooms, run LayoutVLM and I-Design, and have five human raters who do not know which method produced each layout rank them on physical plausibility and semantic alignment; if the human median no longer favors LayoutVLM (as the reported Kendall's tau of 0.46–0.61 between users and GPT-4o already hints), the claimed PSA improvement is an artifact of the automated scorer rather than a real layout-quality gain.

Watch

Extended reading notes

Core claim

The central claim is that a scene layout representation made of two mutually reinforcing parts — numerical estimates of object poses and a set of spatial relations with differentiable objective functions — lets a vision-language model generate 3D layouts that are both physically plausible and semantically aligned with open-ended instructions. The VLM produces the initial poses and the relations from visually marked images; self-consistent decoding keeps only the relations that the initial poses already satisfy, assuming those encode the most critical semantics; and projected gradient descent then jointly minimizes the semantic loss plus a Distance-IoU collision-avoidance loss. The paper reports that this beats LayoutGPT, Holodeck, and I-Design on physical plausibility (collision-free and in-boundary scores) and on positional and rotational coherence, with a PSA score of 58.8 averaged over 11 room types versus 18.0 for I-Design. It further claims that fine-tuning both GPT-4o and the open-source LLaVA-NeXT-Interleave on representations extracted automatically from 3D-Front scenes improves layout generation, with the open-source model gaining the most.

Load-bearing premise

The headline improvement is measured by GPT-4o scores, and if that model's ratings favor outputs produced by its own family of models, the 40.8-point PSA advantage could shrink or disappear under a purely human evaluation.

Editorial extensions

If this is right

  • If the claim holds, open-ended language instructions can drive physically valid 3D scene generation for simulation and robotics training, without requiring predefined object categories or manual layout constraints.
  • The differentiable spatial-relation representation lets unconventional instructions (e.g., stacking tables, placing chairs on tables) be executed while still preventing collisions and boundary violations.
  • The scene layout representation can be extracted automatically from existing scene datasets, so fine-tuning open-source VLMs on it transfers to unseen assets and new object categories in residential rooms.
  • The self-consistency filter, which keeps only relations consistent with the initial poses, offers a cheap way to make VLM spatial planning more coherent without additional supervision.
  • The method reduces out-of-bound placements drastically compared with direct pose prediction, suggesting that optimization is a necessary complement to VLM-generated initial guesses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test whether the same two-representation idea generalizes beyond room layouts; a natural extension would be robotic manipulation goal configurations or graph drawing, where a model proposes both a coarse arrangement and relational constraints that a differentiable optimizer refines.
  • The reported 40.8-point PSA gain is scored by GPT-4o, and the paper's own user study shows only moderate user-to-GPT-4o agreement (Kendall's tau 0.46–0.61); a blind human evaluation across all 11 room types would tell whether the advantage persists outside the automated scorer.
  • Because self-consistent decoding keeps only relations the initial poses already satisfy, it may under-represent instructions whose semantics require a pose change; a testable variant would relax the threshold epsilon or weight relations by confidence instead of hard-filtering them.
  • The ablation table suggests that removing spatial constraints drops the in-boundary score from 92.9 to 14.1, so the physics loss is doing most of the boundary enforcement; measuring exact mesh intersections (rather than bounding-box overlap) would verify that the claimed physical plausibility holds for real asset geometry.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces LayoutVLM, a framework for open-universe 3D layout generation from natural language instructions and unlabeled 3D assets. The method uses a vision-language model (GPT-4o) to produce two complementary representations from visually marked renderings: numerical pose estimates and a set of differentiable spatial relations (distance, on-top-of, align-with, point-towards, against-wall). A self-consistent decoding step retains only those spatial relations that are already satisfied by the initial numerical poses, and the final layout is obtained by projected gradient descent over a combined semantic and physics (DIoU, boundary) objective. The authors evaluate against LayoutGPT, Holodeck, and I-Design across 11 room types, reporting physical plausibility (CF, IB), semantic coherence (Pos., Rot.), and a physically grounded composite (PSA). They also report ablations, a small human study, and fine-tuning experiments showing that open-source VLMs (LLaVA-NeXT-Interleave) improve when fine-tuned on the proposed scene representation extracted from 3D-Front.

Significance. If the empirical claims hold, the paper makes a useful contribution by combining the semantic flexibility of VLMs with a differentiable optimization stage that enforces physical plausibility. The scene layout representation is simple yet flexible, and the ablations (Table 5) provide evidence for the role of each component: visual prompting, self-consistent decoding, spatial constraints, and numerical initialization. The fine-tuning results (Table 6) are also of practical value, showing that the representation can transfer to open-source models. The main weakness is evaluative: the headline PSA gain (40.8 points over I-Design) is driven largely by the in-boundary score, and the semantic scorer is GPT-4o, the same model family that generates the layouts. The human validation with five graduate students shows only moderate user-GPT-4o agreement (Kendall's tau 0.46-0.61). As a result, the magnitude of the claimed semantic advantage over baselines is not yet convincingly established.

major comments (3)
  1. [Section 5.1 and Table 2 (also abstract)] The headline claim "better aligned with the semantic intent" rests on the PSA metric, defined in Section 5.1 as the GPT-4o semantic rating weighted by physical plausibility (CF/IB). Table 2 shows that LayoutVLM's average IB is 94.9 versus I-Design's 34.3, while the raw semantic scores differ less (Pos. 77.5 vs 68.3; Rot. 73.1 vs 62.8). The 40.8-point PSA gap is therefore attributable mostly to the physical plausibility factor, not to superior semantic understanding. The authors should report semantic scores conditioned on physically feasible layouts, or use a metric that does not zero out infeasible layouts, to support the semantic-alignment claim.
  2. [Sections 4.2, 5.1, A.4, B.1] GPT-4o is used both as the generator of the scene layout representation (Section 4.2), as the annotator of asset fronts and descriptions (A.4), as the generator and verifier of test-case assets (B.1), and as the scorer of semantic coherence and PSA (Section 5.1). This creates a risk of self-preference: the evaluator may systematically favor layouts produced by models of its own family. The human validation in Section 5.3 uses only five graduate students, and Table 4 reports user-GPT-4o Kendall's tau values of 0.46-0.61, which are moderate rather than strong. The authors should strengthen the evaluation with a larger human study, an independent scoring model, or rule-based semantic checks to substantiate the claim that GPT-4o is a reliable evaluator in this setting.
  3. [Section 5.2 and Table 2] The paper states "Our method achieves significantly improved performance over existing methods" but Table 2 reports no error bars, confidence intervals, or significance tests. Since the ablations in Table 5 include error bars, the main comparison should similarly report variance across the 11 room types and perform appropriate paired tests (e.g., Wilcoxon signed-rank) to support the word "significantly."
minor comments (5)
  1. [Equation (3)] The distance objective uses the notation ||pi - pj|| in one place and ||posi - pj|| in the defining equation; it should consistently use the planar distance between assets, since the text says the distance is measured in the x-y plane.
  2. [Section 4.2] The epsilon threshold for self-consistent decoding in Eq. (1) is not specified anywhere; for reproducibility, the authors should report its value or the criterion used to set it.
  3. [Section A.3] The implementation details in the supplementary (enforcing at most one orientational constraint per asset, and excluding the on-top-of relation from self-consistent decoding) are not mentioned in the main paper, yet they affect the method's behavior; these details should be summarized in the main text.
  4. [Section 5.3] The reference for Kendall's Tau appears as an unresolved citation placeholder "[?]." Please add the proper reference.
  5. [Table 2] The table is dense and it is hard to see the per-room-type differences. Consider adding error bars or at least marking the average row more clearly, and reporting the number of test cases per room type.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the method's outputs are not equated with its inputs by construction, and evaluator overlap is a validity concern rather than a demonstrated reduction.

full rationale

Walking the derivation chain, the paper takes language instructions and asset renderings as inputs, asks a VLM to emit both numerical pose estimates and spatial relations (Section 4.2), filters the relations by self-consistency in Eq. 1, and then jointly optimizes the poses with an external physics objective—DIoU collision loss plus boundary projection (Eq. 2 and Section 4.3). The final layout is not a restatement of the VLM's initial poses: optimization can move objects, and the ablations (Table 5) show that removing optimization, visual input, or self-consistency measurably changes physical and semantic scores. The physics loss and projection are not defined in terms of the semantic evaluation metric, and the fine-tuning experiment (Table 6) uses ground-truth 3D-Front layouts, not the model's own outputs. The only self-citation, Holodeck [5], supplies a baseline and an asset-annotation convention, not a load-bearing premise. The most serious concern is evaluative rather than derivational: GPT-4o both generates the scene representation (Section 4.2) and scores final renderings (Section 5.1), and PSA multiplies the GPT-4o semantic rating by physical-plausibility terms that LayoutVLM directly optimizes, so part of the 40.8-point PSA gain may reflect metric design and possible self-preference. The paper partially mitigates this with a five-person study showing moderate user-GPT-4o agreement (Kendall's tau 0.46-0.61), but this remains an external-validity caveat, not a circular derivation. No equation in the paper reduces a claimed prediction to its own input, so the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No invented physical entities. The method relies on several hand-chosen thresholds and domain assumptions, most notably the GPT-4o-as-evaluator assumption and the sufficiency of five spatial relations. The 'free' parameters are design choices rather than fitted constants, but they are not fully specified, which limits reproduction.

free parameters (4)
  • epsilon threshold for self-consistent decoding = not reported
    Eq. (1) uses a threshold epsilon to decide whether a predicted spatial relation is consistent with the initial predicted pose; this threshold is not specified and controls which relations enter L_semantic.
  • Heuristic thresholds for extracting spatial relations from 3D-Front = not reported
    Section 4.4 says the method computes cost values for spatial relations using heuristic thresholds to determine whether each relation is satisfied; these thresholds are not given and shape the fine-tuning data.
  • Optimization hyperparameters = 400 steps, projection every 100, Adam, LR decay 0.96, unweighted sum L_semantic+L_physics
    Section A.2 reports these choices by hand; the balance between semantic and physics losses is a design decision that affects results.
  • Visual mark spacing = 2 meters
    Section 4.2 states coordinate points are spaced 2 meters apart to help the VLM gauge scale; this choice influences the VLM's pose estimates.
assumptions (4)
  • domain assumption Input 3D objects are upright and front-facing orientations are determined by GPT-4o.
    Section 3 assumes upright objects and uses an off-the-shelf VLM to annotate front-facing orientations; if these are wrong, rotation-dependent spatial relations break.
  • ad hoc to paper The five spatial relations (distance, on_top_of, align_with, point_towards, against_wall) are sufficient to capture semantics of diverse layout instructions.
    Section 4.1 introduces these five relations as expressive enough; no completeness argument is given, and instructions that require relations like left_of are not directly representable.
  • ad hoc to paper Spatial relations that are consistent with the VLM's initial numerical pose estimates are the most critical semantics to preserve.
    This is the core hypothesis of self-consistent decoding in Section 4.2; if the initial poses are poor, the filter may discard the relations needed to recover the intended layout.
  • domain assumption GPT-4o ratings correlate with human judgments of layout quality.
    Section 5.1 uses GPT-4o as the semantic evaluator; the paper reports a user study with 5 raters and Kendall's Tau 0.46-0.61, which is moderate rather than high.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models." pith.science (2026). https://pith.science/paper/S3YU4SGR

@misc{pith2026241202193,
  author       = {Pith},
  title        = {Pith review of: LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S3YU4SGR}},
  note         = {Machine review of arXiv:2412.02193}
}
read the original abstract

Spatial reasoning is a fundamental aspect of human cognition, enabling intuitive understanding and manipulation of objects in three-dimensional space. While foundation models demonstrate remarkable performance on some benchmarks, they still struggle with 3D reasoning tasks like arranging objects in space according to open-ended language instructions, particularly in dense and physically constrained environments. We introduce LayoutVLM, a framework and scene layout representation that exploits the semantic knowledge of Vision-Language Models (VLMs) and supports differentiable optimization to ensure physical plausibility. LayoutVLM employs VLMs to generate two mutually reinforcing representations from visually marked images, and a self-consistent decoding process to improve VLMs spatial planning. Our experiments show that LayoutVLM addresses the limitations of existing LLM and constraint-based approaches, producing physically plausible 3D layouts better aligned with the semantic intent of input language instructions. We also demonstrate that fine-tuning VLMs with the proposed scene layout representation extracted from existing scene datasets can improve their reasoning performance.

Figures

Figures reproduced from arXiv: 2412.02193 by the authors.

Figure 1
Figure 1. From unlabeled 3D assets and language instruction, LAYOUTVLM generates scene layouts that are physically plausible and semantically coherent—two criteria that existing methods often struggle to meet. Our approach addresses this by using a VLM to generate a scene layout representation that defines both an initial layout and spatial relations between assets for differentiable optimization. Abstract Spatial reasoning i… view at source ↗
Figure 2
Figure 2. Example Scene Representation. Example of our scene representation for a bedroom. Our scene representation consists of numerical estimates of object poses and spatial relations correspond￾ing to objective functions on these poses. Having the VLMs gener￾ate the initial estimates allows us to exploit the semantic knowledge in the large models, and having spatial relations amenable to opti￾mization allows us to generate… view at source ↗
Figure 3
Figure 3. LAYOUTVLM. We illustrate the proposed process of generating 3D scene layout with Vision-Language Models. to produce the final object placements through optimization: arg min {pi}N i=1 (Lsemantic + Lphysics), initial solution {pˆi} N i=1. where Lsemantic is the objective function decoded from the scene layout representation and Lphysics is the objective func￾tion we employ to ensure physical plausibility. In the foll… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative Comparison. We compare with baseline methods in generating layouts based on detailed language instructions. Our method is able to generate layouts that closely follow the instructions and adhere to physical constraints. Bedroom Living Room Dining Room Books…
Figure 5
Figure 5. Figure 5: Examples of Following Detailed Instructions. We show the same set of assets arranged with different language instructions. The latter two examples show that LAYOUTVLM can closely follow the prompts even when the desired layouts are unconventional. the proposed scene la…
Figure 6
Figure 6. Figure 6: Comparison when w/ and w/o self-consistency decoding. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: More qualitative comparison with baseline methods in generating layouts based on detailed language instructions. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion

    cs.CV 2026-07 conditional novelty 6.0 of 10

    SynCity 3000 generates large, coherent 3D scenes from text by fine-tuning an image-to-3D diffusion model to operate convolutionally on overlapping windows, trained on procedurally generated synthetic scene data.

  2. SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis

    cs.GR 2025-08 conditional novelty 6.0 of 10

    SemLayoutDiff uses a categorical diffusion model over top-down semantic maps, conditioned on architectural room masks, to generate coherent 3D indoor layouts across room types.

  3. Canvas3D: Empowering Precise Spatial Control for Image Generation with Constraints from a 3D Virtual Canvas

    cs.HC 2025-08 conditional novelty 6.0 of 10

    Canvas3D lets users arrange objects in a 3D canvas generated from a text prompt, then feeds depth, skeleton, and lighting constraints to diffusion models to produce images that match the layout.

  4. 3D-Generalist: Self-Improving Vision-Language-Action Models for Crafting 3D Worlds

    cs.GR 2025-07 conditional novelty 6.0 of 10

    A self-improving vision-language-model policy iteratively crafts 3D environments from text, and renderings of those environments serve as effective synthetic pretraining data for vision models.

  5. IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A benchmark that scores vision-language models by reconstructing the 3D scene behind an image as executable Blender code finds the models fail mainly on spatial precision, not tool usage.

  6. Video Perception Models for 3D Scene Synthesis

    cs.CV 2025-06 conditional novelty 6.0 of 10

    VIPScene synthesizes 3D scenes by generating a video with Cosmos, reconstructing it with Fast3R, extracting objects with Grounded-SAM and MASt3R, and assembling them from Objaverse assets.

  7. Handle-based Mesh Deformation Guided By Vision Language Model

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A VLM selects deformation handles and target positions, and multi-view voting produces a text-guided mesh deformation with low distortion.

  8. ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ReSpace is an autoregressive LLM framework for text-driven 3D indoor scene editing and synthesis, using a structured JSON scene representation and a voxelization-based layout metric.

  9. Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Scenethesis integrates LLM planning, vision-guided layout refinement, and SDF-based collision and stability optimization to generate physically plausible interactive 3D scenes from text.

  10. ScanEdit: Hierarchically-Guided Functional 3D Scan Editing

    cs.CV 2025-04 conditional novelty 6.0 of 10

    ScanEdit uses hierarchical scene graphs and LLM-based planning, placement, and optimization to rearrange objects in real-world 3D scans from text instructions.

  11. CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step

    cs.CV 2025-07 conditional novelty 4.0 of 10

    CoT-Diff couples a multimodal LLM's step-by-step 3D layout reasoning into the diffusion denoising loop, claiming large gains in spatial alignment for text-to-image generation.

Reference graph

Works this paper leans on

54 extracted references · 36 canonical work pages · cited by 11 Pith papers

  1. [1]

    Lego-net: Learning regular rearrangements of ob- jects in rooms

    Qiuhong Anna Wei, Sijie Ding, Jeong Joon Park, Rahul Sajnani, Adrien Poulenard, Srinath Sridhar, and Leonidas Guibas. Lego-net: Learning regular rearrangements of ob- jects in rooms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19037– 19047, 2023. 2

    2023

  2. [2]

    House- gan++: Generative adversarial layout refinement network towards intelligent computational agent for professional archi- tects

    Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, and Yasutaka Furukawa. House- gan++: Generative adversarial layout refinement network towards intelligent computational agent for professional archi- tects. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 13632–13641, 2021

    work page 2021

  3. [3]

    Atiss: Autoregres- sive transformers for indoor scene synthesis

    Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, and Sanja Fidler. Atiss: Autoregres- sive transformers for indoor scene synthesis. Advances in Neural Information Processing Systems , 34:12013–12026,

  4. [4]

    Layoutgpt: Compositional visual plan- ning and generation with large language models

    Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani, Ar- jun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang. Layoutgpt: Compositional visual plan- ning and generation with large language models. Advances in Neural Information Processing Systems, 36, 2024. 2, 7

    work page 2024

  5. [5]

    Holodeck: Language guided gen- eration of 3d embodied ai environments

    Yue Yang, Fan-Yun Sun, Luca Weihs, Eli VanderBilt, Al- varo Herrasti, Winson Han, Jiajun Wu, Nick Haber, Ranjay Krishna, Lingjie Liu, et al. Holodeck: Language guided gen- eration of 3d embodied ai environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16227–16237, 2024. 2, 3, 7, 4

    work page 2024

  6. [6]

    Controlroom3d: Room generation using semantic proxy rooms

    Jonas Schult, Sam Tsai, Lukas Höllein, Bichen Wu, Jialiang Wang, Chih-Yao Ma, Kunpeng Li, Xiaofang Wang, Felix Wimbauer, Zijian He, et al. Controlroom3d: Room generation using semantic proxy rooms. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6201–6210, 2024. 2

    work page 2024

  7. [7]

    Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting

    Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhiwei Lin, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting. arXiv preprint arXiv:2402.07207, 2024

    arXiv 2024

  8. [8]

    Compositional 3d scene generation using locally conditioned diffusion

    Ryan Po and Gordon Wetzstein. Compositional 3d scene generation using locally conditioned diffusion. In 2024 In- ternational Conference on 3D Vision (3DV), pages 651–663. IEEE, 2024

    work page 2024

Show all 54 references
  1. [9]

    Disentangled 3d scene generation with layout learning

    Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A Efros, and Aleksander Holynski. Disentangled 3d scene generation with layout learning. arXiv preprint arXiv:2402.16936, 2024. 2

    2024 arXiv

  2. [10]

    Lay-a-scene: Personalized 3d object arrangement using text-to-image priors

    Ohad Rahamim, Hilit Segev, Idan Achituve, Yuval Atzmon, Yoni Kasten, and Gal Chechik. Lay-a-scene: Personalized 3d object arrangement using text-to-image priors. arXiv preprint arXiv:2406.00687, 2024. 2

    2024 arXiv

  3. [11]

    Any- home: Open-vocabulary generation of structured and textured 3d homes

    Rao Fu, Zehao Wen, Zichen Liu, and Srinath Sridhar. Any- home: Open-vocabulary generation of structured and textured 3d homes. In European Conference on Computer Vision , pages 52–70. Springer, 2025

    2025

  4. [12]

    Instructscene: Instruction- driven 3d indoor scene synthesis with semantic graph prior

    Chenguo Lin and Yadong Mu. Instructscene: Instruction- driven 3d indoor scene synthesis with semantic graph prior. arXiv preprint arXiv:2402.04717, 2024. 2

    2024 arXiv

  5. [13]

    I-design: Personal- ized llm interior designer

    Ata Çelen, Guo Han, Konrad Schindler, Luc Van Gool, Iro Armeni, Anton Obukhov, and Xi Wang. I-design: Personal- ized llm interior designer. arXiv preprint arXiv:2404.02838,

  6. [14]

    Open-universe indoor scene generation using llm program synthesis and uncurated object databases

    Rio Aguina-Kang, Maxim Gumin, Do Heon Han, Stew- art Morris, Seung Jean Yoo, Aditya Ganeshan, R Kenny Jones, Qiuhong Anna Wei, Kailiang Fu, and Daniel Ritchie. Open-universe indoor scene generation using llm program synthesis and uncurated object databases. arXiv preprint arXi...

    2024 arXiv

  7. [15]

    3d-llm: Injecting the 3d world into large language models

    Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan. 3d-llm: Injecting the 3d world into large language models. Advances in Neural Information Processing Systems, 36:20482–20494, 2023. 3

    2023

  8. [16]

    3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination

    Jianing Yang, Xuweiyi Chen, Nikhil Madaan, Madhavan Iyen- gar, Shengyi Qian, David F Fouhey, and Joyce Chai. 3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination. arXiv preprint arXiv:2406.05132, 2024

    2024 arXiv

  9. [17]

    Scene-llm: Extending language model for 3d visual understanding and reasoning

    Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, and Wen- han Xiong. Scene-llm: Extending language model for 3d visual understanding and reasoning. arXiv preprint arXiv:2403.11401, 2024

    2024 arXiv

  10. [18]

    Ll3da: Visual interactive instruction tuning for omni-3d understand- ing reasoning and planning

    Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, and Tao Chen. Ll3da: Visual interactive instruction tuning for omni-3d understand- ing reasoning and planning. In CVPR, 2024

    2024

  11. [19]

    Gpt4point: A unified framework for point-language understanding and generation

    Zhangyang Qi, Ye Fang, Zeyi Sun, Xiaoyang Wu, Tong Wu, Jiaqi Wang, Dahua Lin, and Hengshuang Zhao. Gpt4point: A unified framework for point-language understanding and generation. In CVPR, 2024

    2024

  12. [20]

    Pointllm: Empowering large language models to understand point clouds

    Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiang- miao Pang, and Dahua Lin. Pointllm: Empowering large language models to understand point clouds. In ECCV, 2024

    2024

  13. [21]

    Chat-scene: Bridging 3d scene and large language models with object identifiers

    Haifeng Huang, Yilun Chen, Zehan Wang, Rongjie Huang, Runsen Xu, Tai Wang, Luping Liu, Xize Cheng, Yang Zhao, Jiangmiao Pang, et al. Chat-scene: Bridging 3d scene and large language models with object identifiers. NeurIPS, 2024. 3

    2024

  14. [22]

    Spatialvlm: Endowing vision-language models with spatial reasoning capabilities

    Boyuan Chen, Zhuo Xu, Sean Kirmani, Brain Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14455–1446...

    2024

  15. [23]

    Spatial- rgpt: Grounded spatial reasoning in vision language model

    An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Rui- han Yang, Jan Kautz, Xiaolong Wang, and Sifei Liu. Spatial- rgpt: Grounded spatial reasoning in vision language model. arXiv preprint arXiv:2406.01584, 2024. 3

    2024 arXiv

  16. [24]

    Scene- script: Reconstructing scenes with an autoregressive struc- tured language model

    Armen Avetisyan, Christopher Xie, Henry Howard-Jenkins, Tsun-Yi Yang, Samir Aroudj, Suvam Patra, Fuyang Zhang, Duncan Frost, Luke Holland, Campbell Orme, et al. Scene- script: Reconstructing scenes with an autoregressive struc- tured language model. arXiv preprint arXiv:2403.13064 ,

    arXiv

  17. [25]

    Agent3d-zero: 10 An agent for zero-shot 3d understanding

    Sha Zhang, Di Huang, Jiajun Deng, Shixiang Tang, Wanli Ouyang, Tong He, and Yanyong Zhang. Agent3d-zero: 10 An agent for zero-shot 3d understanding. arXiv preprint arXiv:2403.11835, 2024. 3

    2024 arXiv

  18. [26]

    Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v

    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. 3, 5

    2023 arXiv

  19. [27]

    Making large multimodal models understand arbitrary visual prompts

    Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P Meyer, Yuning Chai, Dennis Park, and Yong Jae Lee. Making large multimodal models understand arbitrary visual prompts. In CVPR, 2024

    2024

  20. [28]

    Scaffolding coordinates to promote vision-language coordination in large multi-modal models

    Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li, and Yang Liu. Scaffolding coordinates to promote vision-language coordination in large multi-modal models. arXiv preprint arXiv:2402.12058, 2024. 3

    2024 arXiv

  21. [29]

    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. 3, 6

    2023 arXiv

  22. [30]

    Self-consistency improves chain of thought reasoning in lan- guage models

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in lan- guage models. In ICLR, 2023. 5

    2023

  23. [31]

    Distance-iou loss: Faster and better learning for bounding box regression

    Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren. Distance-iou loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence, 2020. 5

    2020

  24. [32]

    Iou loss for 2d/3d object detection

    Dingfu Zhou, Jin Fang, Xibin Song, Chenye Guan, Junbo Yin, Yuchao Dai, and Ruigang Yang. Iou loss for 2d/3d object detection. In 2019 international conference on 3D vision (3DV), pages 85–94. IEEE, 2019. 5

    2019

  25. [33]

    Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models

    Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models. arXiv:2407.07895, 2024. 6, 9

    2024 arXiv

  26. [34]

    Objaverse: A universe of annotated 3d objects

    Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec...

    2023

  27. [35]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22227–2...

    2024

  28. [36]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  29. [37]

    Based on the assets, you should describe the general layout of the scene, the types of assets present, and any notable features

    You will first be provided a list of assets. Based on the assets, you should describe the general layout of the scene, the types of assets present, and any notable features

  30. [38]

    You should consider the functional, semantic, and geometric relationships between the assets

    You will identify the semantic relationships between the assets. You should consider the functional, semantic, and geometric relationships between the assets

  31. [39]

    You should explain the rationale behind each group and how the assets within each group are related to each other

    You will then describe how you would group the assets into semantic asset groups. You should explain the rationale behind each group and how the assets within each group are related to each other

  32. [40]

    You should consider the significance of each group and the logical flow of the scene layout

    You will then order the semantic asset groups based on the sequence in which they should be placed in the scene. You should consider the significance of each group and the logical flow of the scene layout. For example, larger or more prominent assets may be placed first to est...

  33. [41]

    **Example:** Suppose you are examining a bedroom scene

    Finally, you will format the grouping information into a clear and organized structure that can be easily understood by other designers or stakeholders. **Example:** Suppose you are examining a bedroom scene. In the bedroom, there are the following assets: bed | ... nightstand...

  34. [42]

    After examining the scene, you will describe the scene a bedroom with a bed and a seating area for dressing

  35. [43]

    The bedside table should be close to the bed for easy access

    You will list the assets and their relationships : - the bed is the central piece - the nightstand is next to the bed for placing items. The bedside table should be close to the bed for easy access. - the lamp is on the nightstand for lighting. The lamp should be close to the ...

  36. [44]

    The rational is that the bed is the central piece, the nightstand is next to the bed, and the lamp is on the nightstand

    You will group the assets into semantic asset groups: - Group 1: Bed, Nightstand, Lamp. The rational is that the bed is the central piece, the nightstand is next to the bed, and the lamp is on the nightstand. They are related to each other because they are used for sleeping an...

  37. [45]

    They should be placed first to establish the sleeping area

    You will order the semantic asset groups based on the sequence in which they should be placed in the scene: - Group 1: Bed, Nightstand, Lamp. They should be placed first to establish the sleeping area. They are the focal point of the room. - Group 2: End of bed bench. It shoul...

  38. [46]

    list": [ {

    You will format the grouping information into a clear and organized structure: ‘‘‘json { "list": [ {"id": 1, "name": "sleeping area", "assets": ["bed", "nightstand", "lamp"], "rational": "they are used for sleeping and reading.", "key_relations_between_assets": ["the bed is th...

  39. [47]

    High-level design goals

  40. [48]

    A list of existing scene assets ( if any )

  41. [49]

    A list of new assets with their dimensions and orientations

  42. [50]

    Walls are also labeled with orientation arrows

    A top-down view of the current scene, with a marked global frame, 1-meter grid, labeled assets, and front-facing orientation arrows. Walls are also labeled with orientation arrows

  43. [51]

    A side view of the current scene, with the global frame and 1-meter grid

  44. [52]

    Your task is to write a program that:

    A top-down view of each new asset in an empty scene, facing the positive X-axis, labeled with its name and front-facing arrow. Your task is to write a program that:

  45. [53]

    Specifies precise position and rotation for the new asset placements. 3

  46. [54]

    point towards

    Constraints for the asset placements. These constraints will ensure that the layout semantics are maintained when the layout is being adjusted to be physically feasible. **Instructions:** Follow these instructions carefully: - Specify the constraints for all the assets to be p...

    1980

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.