Steerable Scene Generation with Post Training and Inference-Time Search

Hongkai Dai; Nicholas Pfaff; Russ Tedrake; Sergey Zakharov; Shun Iwase

REVIEW 4 major objections 5 minor 59 references

Steerable Scene Generation with Post Training and Inference-Time Search

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A scene diffusion model trained on 44 million procedurally generated scenes can be steered toward objectives such as higher clutter through RL post training, conditional generation, or inference-time tree search, without retraining from…

desk verdict A solid systems paper with a genuinely useful released dataset; the feasibility 'guarantee' is softer than advertised, but the core steering story survives. read the letter →

arxiv 2505.04831 v2 pith:2VE6CKAA submitted 2025-05-07 cs.RO cs.GRcs.LG

Nicholas Pfaff , Hongkai Dai , Sergey Zakharov , Shun Iwase , Russ Tedrake This is my paper · ORCID

classification cs.ROcs.GRcs.LG

keywords SE(3)scenegenerationdiffusionmodelsMonteCarlotreesearchreinforcementlearningpost-trainingproceduralconditionalphysicalfeasibilityrobotsimulation

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

This paper argues that a single pretrained generative model over full six-degree-of-freedom scenes can be adapted to downstream objectives without being retrained from scratch. The authors train a diffusion model on more than 44 million procedurally generated scenes spanning five environments, where a scene is an unordered set of objects drawn from a fixed asset library, each placed at a full six-degree-of-freedom $\mathrm{SE}(3)$ pose. They then demonstrate three steering routes: reinforcement-learning post training, text-conditioned or inpainting-based conditional generation, and an inference-time Monte Carlo tree search that incrementally repairs and completes scenes while optimizing a task reward. If the claim holds, robots can obtain task-specific training scenes — high clutter, stable arrangements, prompt-specified layouts — from one flexible prior, with physical feasibility enforced afterwards by collision-free projection followed by gravity settling in simulation.

What carries the argument

The central object is the mixed discrete-continuous $\mathrm{SE}(3)$ scene diffusion model: a transformer-based denoiser, trained to be permutation-equivariant over unordered object sets, in which each object is a one-hot asset category diffused discretely together with a translation vector and a nine-dimensional rotation matrix diffused continuously and projected onto $\mathrm{SO}(3)$ at sampling time. It carries the argument because it is the single pretrained prior that all three steering strategies act on, and it makes steering possible by being differentiable, conditionable, and cheap to sample. The argument is completed by two feasibility mechanisms: a signed-distance projection solved with SNOPT that removes inter-object penetration while keeping orientations fixed, and a Drake simulation step that lets unstable objects settle under gravity for 0.1 seconds; together these define the 'physically feasible' label that serves as the MCTS reward, the inpainting mask criterion, and the paper's claim that outputs are simulation-ready.

What would settle it

Generate 1,000 scenes from the released Restaurant model, run the stated post-processing, and record how many fail the SNOPT projection step outright, then re-simulate the surviving 'stable' scenes for 10 seconds to see whether objects judged at rest after 0.1 seconds stay at rest. A substantial fraction of failures on either check would show that the feasibility guarantee, and the MCTS reward built on it, do not hold as stated.

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Extended reading notes

Core claim

The paper's central claim is that a scene generative model trained on broad procedural data can be steered toward task-specific objectives, such as increasing clutter, and that this steering works through three complementary mechanisms: reinforcement-learning post training with a task reward, conditional generation via text prompts and structured inpainting, and a Monte Carlo tree search that builds scenes incrementally at inference time. The pretrained model predicts which objects to place from a fixed asset library together with their continuous $\mathrm{SE}(3)$ poses, using a mixed discrete-continuous diffusion objective over permutation-equivariant object sets; because the model is differentiable and fast to sample from, its output distribution can be shifted after the fact. The authors treat physical feasibility as a separate guarantee rather than leaving it to the diffusion model: every generated scene is projected to a collision-free configuration and then settled under gravity in simulation, so outputs are claimed to be non-penetrating, statically stable, and directly usable for robot data collection. The reported experiments show scenes matching dataset statistics on all five scene types, inference-time search reaching the 34-object maximum of a Dimsum dataset from a 17.1-object average, and RL post training producing shelf scenes denser than anything in the pretraining distribution.

Load-bearing premise

The load-bearing premise is that physical feasibility is fully captured by two mechanical checks — a signed-distance projection solved with SNOPT and a 0.1-second Drake settling simulation with its stability thresholds — so the paper's guarantee that outputs are physically correct (Section 1), and the MCTS reward that counts objects passing those checks, both inherit all their trust from an optimizer that the appendix itself concedes may fail to converge on complex Restaurant scenes.

Editorial extensions

If this is right

A single pretrained scene model can be redirected toward an objective its training data never emphasized: the paper shows object-count rewards driving denser shelves after RL post training, and MCTS pushing a Dimsum scene to the 34-object maximum from a training average of 17.1 objects.
Generated scenes can be handed to a robot simulator without manual correction, since the projection-plus-simulation pipeline is designed to eliminate penetrations and unstable equilibria; the authors demonstrate this by teleoperating a mobile manipulator through pick-and-place interactions in Drake.
The three steering routes compose: a model refined by RL can still be prompted by text or searched at inference time, so scene generation gains the same kind of post-hoc alignment workflow used for large language models.
The released resource, over 44 million full six-degree-of-freedom scenes across five environments, gives future work a benchmark beyond the 18,968 floor-plan layouts of 3D-FRONT, with manipulable small objects rather than static furniture.
Cotraining across scene types yields a single model that can interpolate between environments, generating scenes that mix object identities from the Living Room Shelf and Pantry Shelf datasets when prompted with both.

Reading between the lines

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

If the steering claim generalizes, scene generation inherits an alignment workflow: pick the cheapest of RL, conditioning, or search for each new objective, and reuse the same broad prior. The paper's separation of the MCTS mask generator from the reward function is a natural interface for arbitrary non-differentiable goals such as grasp success or human preference.
A testable extension the paper does not run is to keep the MCTS machinery but replace the feasibility-count reward with a semantic one, such as the number of requested object categories. The framework's modular design predicts the search should still improve that reward, which would show the steering result is not merely an artifact of the physics post-processor.
The element to stress-test is the feasibility oracle itself: the paper reports that SNOPT can fail to converge in complex Restaurant scenes (Appendix B.4), and stability is judged from 0.1 seconds of simulation, so 'physically feasible' is only as strong as those two checks. A longer simulation horizon or an analytic equilibrium certificate would be the natural safeguard.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague — quick read of arXiv:2505.04831. Worth your time. It builds an SE(3) scene diffusion model on 44 million procedurally generated scenes, then shows three steering methods: DDPO RL post-training, text conditioning/inpainting, and MCTS inference search. What is genuinely new isn't any single component — mixed diffusion, CFG, DDPO, UCT all exist — it's that they are combined in a working scene-generation system with full 6-DOF poses, a fixed asset library, and a released dataset/code. The dataset alone is a resource for anyone doing simulation-based manipulation training.

The quantitative story is mostly solid. Against DiffuScene and MiDiffusion adapted to SE(3), the model wins or ties on CA/FID/APF across the five scene types, with consistently lower MTP. The MCTS demo — reaching the maximum 34 physically feasible dimsum objects from 21 — is nice evidence that inference-time search can push beyond the training distribution. The teleop video supports simulation-readiness. I'd trust the central claim that one pretrained prior can be steered toward clutter or feasibility objectives.

Soft spots, in order of seriousness. First, the "guarantee physical correctness" language in Section 1 and "MTP is guaranteed zero after projection" in Appendix C are overstatements. Appendix B.4 concedes SNOPT may fail to converge on complex Restaurant scenes. There are no convergence statistics, no fallback, and no post-projection penetration measurements. Since the same simulation thresholds define the MCTS mask and reward, a solver failure or misclassified stability directly inflates the headline search result. This doesn't sink the paper, but the authors should replace "guarantee" with a reported success rate, or add failure handling. Second, evaluation is internal to the procedural distribution: models train on synthetic scenes, metrics compare against those same synthetic scenes, and feasibility is judged by the same Drake pipeline used as reward. Some external validation — real scans, a different simulator, human ratings — would strengthen it. Third, several headline metrics lack error bars, and RL post-training is only shown on the continuous variant with one reward. Fourth, the novel-scenes analysis via Sinkhorn nearest neighbors is qualitative but at least addresses memorization.

None of these are fatal; they're fixable in revision. The paper is a serious engineering contribution and deserves referee time. If I were handling it, I'd send it out, with a request for SNOPT convergence numbers and a softened feasibility claim.

Referee Report

4 major / 5 minor

Summary. The paper proposes a framework for SE(3) scene generation in which a diffusion model over object sets (asset ID, translation, and 9D rotation) is trained on procedurally generated scenes and then steered toward downstream objectives by three mechanisms: DDPO-based RL post training, text-conditioned generation and inpainting, and an MCTS procedure that iteratively inpaints masked invalid objects. Physical feasibility is enforced by a nonlinear projection step solved with SNOPT, followed by Drake simulation. The authors report unconditional and conditional generation results on five procedurally generated scene types, compare with DiffuScene and MiDiffusion, demonstrate clutter-maximizing RL and MCTS steering, and release code, model weights, and a dataset of 44.79 million scenes, including a teleoperation demo.

Significance. If the claims hold, the paper makes a meaningful contribution: a large-scale SE(3) scene dataset, a unified scene prior supporting three distinct steering paradigms, and an MCTS inference-time search strategy for diffusion-based scene synthesis. The release of code, data, and weights is a concrete strength that should make the results reproducible. The significance is tempered by two issues: the feasibility guarantee is conditional on a solver convergence caveat that is conceded in Appendix B.4, and the evaluation of physical feasibility and steering quality is largely internal to the same procedural and simulation pipeline used to define and optimize the objectives.

major comments (4)

[§1, Appendix B.4, Appendix C] The Section 1 claim that projection and simulation 'guarantee physical correctness' is not supported for the hardest scenes. Appendix B.4 states that in complex Restaurant scenes 'SNOPT may fail to converge due to the presence of many complicated nonlinear non-convex constraints,' yet Appendix C asserts that 'after projection, MTP is guaranteed to be zero.' The manuscript provides no convergence statistics, no fallback procedure for failed solves, and no post-projection measurements of residual penetration. Because Section 3.5 and Figures 29–31 use the number of physically feasible objects as the MCTS reward, and this number is determined by the same Drake feasibility checks, a solver failure in the high-clutter regime directly affects the headline inference-time search result. Please report SNOPT convergence rates per dataset and scene complexity, specify the fallback behavior, and verify post-projection penetration with measurements rather than an unconditional guarantee.
[§3.5, Appendix B.7] The MCTS evaluation is circular with respect to the feasibility definition. The mask generator and reward function (Appendices B.7.1 and B.7.2) use Drake's signed-distance checker and a 0.1 s settle simulation with 'predefined thresholds' that are never specified, and the same criteria are the only evidence that the final scenes are physically feasible. Please state the translation/rotation thresholds, report the sensitivity of the MCTS reward to those thresholds, and validate the feasibility determinations against an independent source (e.g., a different simulator, longer simulation horizons, or real-world checks) so that the steering objective is not merely conformity to one simulator's settings.
[§3.3, Appendix D.2] The RL post-training section is presented mostly qualitatively. Figure 4 and Figure 19 show sample scenes and a reward curve, but there are no quantitative comparisons of CA, FID, MTP, or the object-count distribution before versus after post training, and no comparison against the other two steering mechanisms on the same clutter objective. Because the central contribution is that post training steers the pretrained model beyond its training distribution, the paper should provide quantitative evidence that the selected checkpoint preserves scene quality while increasing object count, and ideally a comparison with conditional generation and MCTS under a common budget.
[§3.5, §2.4.3] The MCTS contribution lacks a comparison against simpler inference-time baselines. Figure 7 compares MCTS only with the initial inpainting sample and the best of B=3 unconditional samples, and Section 2.4.3 states that the method reduces to Random Search when B→∞, but no random-search or best-of-k baseline is evaluated under the same number of forward passes. Because Dimsum Table scenes have a small maximum object count (34), the claim that MCTS 'reaches the maximum possible 34 objects' would be more persuasive if compared with, for example, best-of-N independent samples or a greedy inpainting baseline using the same reward. Please add such a comparison, including compute cost.

minor comments (5)

[Appendix B.7.1] Please specify the exact stability thresholds used in the 0.1 s Drake simulation; the current text only says 'predefined thresholds.'
[§3.1] Please clarify whether 'MTP' is reported as the median over scenes of total penetration or as a median per-object quantity; the caption of Table 1 calls it 'median total penetration,' which is ambiguous.
[§2.4.3] Please report the hyperparameters used for MCTS (exploration constant c, branching factor B, number of iterations, and noise initialization strategy) in the main text or a table, since Figure 7 reports only B=3 and 313 iterations.
[Appendix D.5] The novelty analysis with Sinkhorn-Knopp distance is a useful addition, but it reports only qualitative nearest-neighbor examples; a quantitative threshold or distribution of distances would strengthen the claim that the model does not memorize training scenes.
[§2.2] Please define how the empty-object token interacts with the discrete diffusion and with the upper bound N on scene size; this affects how variable-size scenes are sampled and how the MCTS mask treats empty slots.

Circularity Check

1 steps flagged · score 3.0 of 10

Physical-feasibility 'guarantee' is self-definitional (Drake defines and scores feasibility), but the steering results rest on independent reward curves and baseline comparisons.

self definitional [Appendix B.7.2 Reward Function; cf. Section 1, Section 2.3, Appendix B.7.1, Appendix C]
"The reward is defined as the number of physically feasible objects in the scene—those that are both non-penetrating and statically stable according to the same criteria used in the mask generator."

The same Drake signed-distance checker and 0.1 s settle simulation define (a) which objects are masked as invalid (B.7.1), (b) the MCTS reward (B.7.2), and (c) the reported physical-feasibility metric MTP, with Appendix C asserting that 'after projection, MTP is guaranteed to be zero.' Section 1 then claims that projection and simulation 'guarantee physical correctness.' Thus a scene is 'physically feasible' by construction exactly when it passes the checks that the pipeline itself performs; the Figure 7 MCTS maximum and the MTP=0 claim are consequences of this operational definition, not an independent validation of real-world physical correctness. The caveat in B.4 (SNOPT 'may fail to converge' in Restaurant scenes) makes even this internal guarantee conditional.

full rationale

The paper's central steering claims are not circular: RL post-training, conditional generation, and MCTS are evaluated against explicit rewards and against external baselines (DiffuScene, MiDiffusion) on shared FID/CA/APF/MTP metrics, and the MCTS search is a genuine optimization procedure rather than a fitted parameter renamed as a prediction. The main circularity risk is confined to the physical-feasibility guarantee, which is defined and scored by the same Drake/SNOPT pipeline it uses to enforce feasibility; this is an operational definition rather than an independent check, and Appendix B.4's admitted SNOPT failures undercut the unconditional phrasing in Section 1. Self-citations ([16] data generator, [44] Drake, [3]/[10] related work) are tooling and prior-work references, not load-bearing uniqueness theorems, and code/data/model weights are released. Overall score 3 reflects one self-definitional feasibility loop while the core adaptation results remain independently demonstrated.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

All ground truth used for evaluation (procedural scenes, Drake-based feasibility, semantic rendering metrics) is internal to the authors' pipeline; no external real-world scene benchmark or downstream robot policy metric is used. The only new artifacts are the dataset and the steerable prior, which are products of the method rather than postulated physical entities.

free parameters (8)

CFG guidance weight w = not reported for main tables; w=5 for interpolation figure
Classifier-free guidance strength is hand-set and directly controls how strongly text conditions shape samples, yet no sweep is reported for the headline tables.
MCTS exploration constant c = not reported
The UCT constant trades exploration against exploitation; the paper gives the formula but no chosen value or sensitivity analysis.
MCTS branching factor B = 3
Only one branching factor is tested; the paper notes B=infinity reduces to random search but does not vary B.
Static equilibrium simulation time and stability thresholds = 0.1 s; thresholds not specified
The definition of physical feasibility depends on hand-set simulation duration and translation/rotation change thresholds, which are never reported.
RL regularization weight lambda_DDPM = 100 to 200
A tuned coefficient stabilizes RL post training; no full sweep is shown, and the chosen value affects the reward-quality tradeoff.
Maximum object capacity increase = +20 for Living Room RL; +30 for shelf MCTS
The scene representation's maximum object count is raised by hand before RL or search to allow extrapolation; this capacity choice is central to the clutter experiments.
CA classifier training iterations = tuned per dataset
Classifier accuracy depends on deliberately tuned classifier strength; the tuning is fixed across methods but the values are not reported.
RL checkpoint selection = step 7000 for Living Room RL
A sampling checkpoint is chosen by hand to avoid overoptimization; no formal selection criterion is given.

assumptions (4)

domain assumption Procedural scene grammars from Izatt and Tedrake provide a faithful proxy for robot-relevant manipulation environments.
Section 2.1 uses a single procedural model as the sole training source, and Section 3.1 evaluates realism against that same source; Section 5 acknowledges real-world complexity is not fully captured.
domain assumption Drake rigid-body simulation and signed-distance collision checking provide ground-truth physical feasibility.
Appendix B.7.1 defines invalid objects by 0.1 s simulation and penetration checks; the same simulator is used for post-processing, MCTS rewards, and the teleoperation check.
domain assumption FID and classifier accuracy on semantic renderings are valid proxies for SE(3) scene generation quality.
Appendix C concedes single viewpoints miss occlusions and that no pre-trained SE(3) feature extractor exists, yet CA and FID are the primary quality metrics.
ad hoc to paper A continuous DDPM variant adequately represents the full model for RL post training.
Section 2.4.1 and the Limitations state RL post training uses a fully continuous model rather than the mixed discrete-continuous model, with extension left as future work.

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Cite this review

Pith. "Pith review of Steerable Scene Generation with Post Training and Inference-Time Search." pith.science (2026). https://pith.science/paper/2VE6CKAA

@misc{pith2026250504831,
  author       = {Pith},
  title        = {Pith review of: Steerable Scene Generation with Post Training and Inference-Time Search},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2VE6CKAA}},
  note         = {Machine review of arXiv:2505.04831}
}

read the original abstract

Training robots in simulation requires diverse 3D scenes that reflect the specific challenges of downstream tasks. However, scenes that satisfy strict task requirements, such as high-clutter environments with plausible spatial arrangement, are rare and costly to curate manually. Instead, we generate large-scale scene data using procedural models that approximate realistic environments for robotic manipulation, and adapt it to task-specific goals. We do this by training a unified diffusion-based generative model that predicts which objects to place from a fixed asset library, along with their SE(3) poses. This model serves as a flexible scene prior that can be adapted using reinforcement learning-based post training, conditional generation, or inference-time search, steering generation toward downstream objectives even when they differ from the original data distribution. Our method enables goal-directed scene synthesis that respects physical feasibility and scales across scene types. We introduce a novel MCTS-based inference-time search strategy for diffusion models, enforce feasibility via projection and simulation, and release a dataset of over 44 million SE(3) scenes spanning five diverse environments. Website with videos, code, data, and model weights: https://steerable-scene-generation.github.io/

Figures

Figures reproduced from arXiv: 2505.04831 by the authors.

**Figure 1.** Overview of our approach. We train a diffusion-based generative model on SE(3) scenes generated by procedural models, then adapt it to downstream objectives via reinforcement learningbased post training, conditional generation, or inference-time search. The resulting scenes are physically feasible and fully interactable. We demonstrate teleoperated interaction in a subset of generated scenes using a mobile KUKA ii… view at source ↗

**Figure 2.** Our MCTS inference-time search. The root node is fully masked (blue), and child nodes represent partially inpainted scenes (blue-green). The rollout node is highlighted with a red halo. 2.4 Steering Scene Generative Models Toward Downstream Objectives A key capability of scene generative models is their potential for steering generation toward downstream goals, even beyond the training distribution. We explore thre… view at source ↗

**Figure 3.** Text-conditioned scene generation. A model trained on the Restaurant (High-Clutter) dataset is queried with the shown text prompt. The generated scene matches both the large-scale layout and fine-grained object details. The search proceeds through the standard MCTS phases [48] (shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (31 more)

**Figure 4.** Figure 4: RL post training with an object count reward. We post-train a model originally trained on the Living Room Shelf dataset. Left: Sample before post training. Middle: Sample after post training. Right: Reward curve. The red line marks the maximum number of objects seen du…

**Figure 5.** Figure 5: Scene rearrangement example. A scene from the Restaurant (Low-Clutter) dataset is rearranged via inpainting by a model trained on the same dataset. Red, green, and blue ellipses highlight corresponding objects. Notably, cutlery is moved from the utensil crock to the ta…

**Figure 6.** Figure 6: Interpolation between Living Room and Pantry Shelf Scenes. We train a joint model on both datasets with a 50/50 batch mix. By prompting for objects unique to each dataset (red = Living Room Shelf, blue = Pantry Shelf), we guide the model to generate interpolated scenes…

**Figure 7.** Figure 7: Inference-time MCTS. We apply MCTS at inference time to generate a Dimsum scene that maximizes the number of physically feasible objects. Left: Initial sample and final result after search. Red, green, and blue ellipses highlight corresponding objects. Note how the sea…

**Figure 8.** Figure 8: Dataset statistics. The object count statistics for each dataset and the combined dataset. The first x-label on each histogram represents the minimum object number, and the last one represents the maximum object number. Note the long tails of the distributions. 15 [P…

**Figure 9.** Figure 9: Denoising network architecture. Our model follows a Flux-style design [42], operating over mixed discrete and continuous scene representations. Discrete and continuous inputs are separately embedded and added, then passed through double stream transformer blocks that …

**Figure 10.** Figure 10: Semantic renderings used to compute image-based metrics such as classifier accuracy [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]

**Figure 11.** Figure 11: Unconditional generation results for a model trained on the Breakfast Table (Low-Clutter [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

**Figure 12.** Figure 12: Unconditional generation results for a model trained on the Breakfast Table (High-Clutter [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

**Figure 13.** Figure 13: Unconditional generation results for a model trained on the Dimsum Table dataset. [PITH_FULL_IMAGE:figures/full_fig_p025_13.png]

**Figure 14.** Figure 14: Unconditional generation results for a model trained on the Living Room Shelf dataset. [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]

**Figure 15.** Figure 15: Unconditional generation results for a model trained on the Pantry Shelf dataset. [PITH_FULL_IMAGE:figures/full_fig_p027_15.png]

**Figure 16.** Figure 16: Unconditional generation results for a model trained on the Restaurant (High-Clutter) [PITH_FULL_IMAGE:figures/full_fig_p028_16.png]

**Figure 17.** Figure 17: Unconditional generation results from a model trained jointly on all our scenes with equal [PITH_FULL_IMAGE:figures/full_fig_p029_17.png]

**Figure 18.** Figure 18: Unconditional generation results from a model trained on the Dimsum dataset after post [PITH_FULL_IMAGE:figures/full_fig_p031_18.png]

**Figure 19.** Figure 19: Unconditional generation results from a model trained on the Living Room Shelf dataset [PITH_FULL_IMAGE:figures/full_fig_p032_19.png]

**Figure 20.** Figure 20: Text conditional generation results for models trained on the Breakfast Table (High [PITH_FULL_IMAGE:figures/full_fig_p033_20.png]

**Figure 21.** Figure 21: Text conditional generation results for models trained on the Living Room Shelf and [PITH_FULL_IMAGE:figures/full_fig_p034_21.png]

**Figure 22.** Figure 22: Text conditional generation results for a model trained on the Restaurant (High-Clutter) [PITH_FULL_IMAGE:figures/full_fig_p035_22.png]

**Figure 23.** Figure 23: Rearrangement results for models trained on the Breakfast Table and Dimsum datasets. [PITH_FULL_IMAGE:figures/full_fig_p036_23.png]

**Figure 24.** Figure 24: Rearrangement results for models trained on the Living Room Shelf and Pantry Shelf [PITH_FULL_IMAGE:figures/full_fig_p037_24.png]

**Figure 25.** Figure 25: Rearrangement results for a model trained on the Restaurant (High-Clutter) dataset. [PITH_FULL_IMAGE:figures/full_fig_p038_25.png]

**Figure 26.** Figure 26: Completion results for models trained on the Breakfast Table and Dimsum datasets. [PITH_FULL_IMAGE:figures/full_fig_p039_26.png]

**Figure 27.** Figure 27: Completion results for models trained on the Living Room Shelf and Pantry Shelf [PITH_FULL_IMAGE:figures/full_fig_p040_27.png]

**Figure 28.** Figure 28: Conditional generation results from a model jointly trained on the Living Room Shelf [PITH_FULL_IMAGE:figures/full_fig_p041_28.png]

**Figure 29.** Figure 29: Inference-time MCTS results for models trained on the Restaurant (Low-Clutter) and [PITH_FULL_IMAGE:figures/full_fig_p042_29.png]

**Figure 30.** Figure 30: Inference-time MCTS results for models trained on the Living Room Shelf dataset. Initial [PITH_FULL_IMAGE:figures/full_fig_p043_30.png]

**Figure 31.** Figure 31: Inference-time MCTS results for a model trained on the Restaurant (High-Clutter) [PITH_FULL_IMAGE:figures/full_fig_p044_31.png]

**Figure 32.** Figure 32: Unconditional samples and their closest dataset scenes for models trained on the Break [PITH_FULL_IMAGE:figures/full_fig_p046_32.png]

**Figure 33.** Figure 33: Unconditional samples and their closest dataset scenes for models trained on the Living [PITH_FULL_IMAGE:figures/full_fig_p047_33.png]

**Figure 34.** Figure 34: Qualitative post processing ablation. Interesting regions are highlighted with blue ellipses. The left column contains samples generated by the model without post processing. The middle column contains samples from the left after applying physics simulation only. The …

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