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REVIEW 4 major objections 4 minor 1 cited by

HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read HiScene's central claim is that a full 3D scene can be created from a text prompt by generating the whole room as one isometric object, then decomposing it and regenerating every item as an independent, aligned 3D instance.

desk verdict Clever isometric-view pipeline with a strong amodal completion module, but the headline claim of separable complete objects is never directly measured and the scene-initialization keystone is unvalidated. read the letter →

arxiv 2504.13072 v1 pith:73K3HPC7 submitted 2025-04-17 cs.GR cs.CVcs.MM

Wenqi Dong , Bangbang Yang , Zesong Yang , Yuan Li , Tao Hu , Hujun Bao , Yuewen Ma , Zhaopeng Cui This is my paper · ORCID
classification cs.GRcs.CVcs.MM
keywords hierarchical3DscenegenerationisometricviewGaussianSplattingamodalcompletionvideodiffusionshapepriorinjectiontext-to-3Dparsing
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 a new route to text-to-3D scene generation: instead of dictating layouts with large language models or lifting a single image into an inseparable mesh, it generates the entire room as one object, then rewrites each item as a separate, manipulatable 3D entity. The payoff, if correct, is that users get natural scene arrangements and intact objects they can edit, replace, or simulate, all from a prompt without any predefined layout. The method depends on isometric views for their low occlusion and consistent scale, on analysis-by-synthesis parsing of an initial 3D Gaussian scene, and on video-diffusion amodal completion followed by spatial shape prior injection to keep regenerated objects coherent with their original positions.

What carries the argument

The load-bearing representation is the isometric view taken as a scene-as-object projection: it makes a room behave like a single object to an object-centric 3D generator. Around that, the machinery is a three-stage pipeline: (1) TRELLIS native 3D generation creates an initial 3D Gaussian Splatting scene from the isometric image; (2) OmniSeg3D-GS contrastive segmentation, fed by EntitySeg instance masks, separates objects, and object-centric circular renders are screened by a vision-language model for occlusion; (3) a fine-tuned Stable Video Diffusion model performs amodal completion as a video transition, and the completed views go through sparse-view LRM whose voxelized geometry is encoded by TRELLIS's structure encoder and injected as a noisy latent initialization at intermediate timestep $t\in[0.2,0.4]$, anchoring regeneration to the original placement.

What would settle it

Take a set of isometric room images with known ground-truth object instances and run TRELLIS scene initialization followed by OmniSeg3D-GS with EntitySeg masks; if overlapping objects such as chairs or table and pillows merge into single Gaussian groups and cannot be separated into the known instances, the decomposition-and-regeneration cascade fails at its first step.

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Extended reading notes

Core claim

The paper's central discovery is that a full 3D scene can be generated compositionally by treating a room as a hierarchical object under an isometric view. The room is first generated as a whole by a native object-level 3D generation model, then parsed into instances through 3D semantic segmentation, each instance is completed with a video-diffusion model that treats occlusion removal as a temporal dissolve transition, and finally each object is regenerated with a shape prior so it keeps the original scene's pose and scale. The authors report that this pipeline yields scenes with natural layouts, complete compositional instances, and physical plausibility, and outperforms LLM-layout baselines on aesthetic score, image-text alignment, and user preference.

Load-bearing premise

The single most load-bearing premise is that the object-centric native 3D generator TRELLIS, trained on single objects, also produces a clean, parseable 3D Gaussian scene when fed a room-level isometric image; the paper invokes this at scene initialization without validation at scene scale.

Editorial extensions

If this is right

  • Users can generate editable 3D scenes from text without specifying a layout, bypassing LLM spatial planning and handcrafted placement rules.
  • Each object in the generated scene is produced as a complete, separable 3D entity, enabling individual manipulation, replacement, and simulation without disturbing the rest of the room.
  • Amodal completion formulated as a temporal video transition recovers occluded object shape and removes occlusion shadows better than static image inpainting or single-image diffusion, as measured on the COCO-A and BSDS-A benchmarks.
  • Spatial shape prior injection keeps regenerated objects aligned with the original scene, avoiding the canonical-orientation drift and Janus artifacts that appear when native 3D regeneration runs without alignment.
  • The full pipeline runs in about 12 minutes per scene, compared with one to two hours for SDS-optimization-based baselines, making interactive iteration practical.

Reading between the lines

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

  • The same hierarchy could be applied recursively, so a generated object that itself contains smaller objects could be parsed and regenerated as a scene within a scene, provided the underlying native 3D generator can represent that level of detail.
  • The video-transition framing of amodal completion is not tied to furniture; the same dissolve-occlusion training recipe could be applied to people, vehicles, or dense vegetation by constructing shadow-aware video pairs for those categories.
  • Because the shape prior is injected as latent initialization rather than a model-specific constraint, the alignment mechanism could be ported to other native 3D generators beyond TRELLIS, which would make the pipeline more modular.
  • A testable extension is to add PBR material estimation into the regeneration stage, directly addressing the paper's own stated limitation of baked lighting and opening the output to standard rendering pipelines.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes HiScene, a text-to-3D-scene generation system that treats a scene as a hierarchy of objects under isometric views. The pipeline first generates an isometric scene image from text, lifts it to a 3D Gaussian Splatting scene via the object-centric native 3D generator TRELLIS, and then parses the scene into instances using EntitySeg and OmniSeg3D-GS. Each occluded instance is completed with a video-diffusion-based amodal completion model trained on a new 1.32M-pair dataset that includes 468K shadow-aware synthetic pairs, and the completed instance is regenerated with a shape-prior injection from sparse-view LRM to preserve spatial alignment. The method is evaluated on 12 scenes with CLIP/ImageReward/Aesthetic scores and a 20-user study, and the amodal completion model is evaluated on COCO-A and BSDS-A. The paper also reports ablations on the video model, shadow data, and spatial alignment, and gives a runtime of about 12 minutes per scene.

Significance. If the central claim holds—that users can obtain complete, separable, spatially aligned 3D objects inside an editable scene—HiScene would be a practically valuable bridge between 2D text-to-image generation and 3D scene editing. The paper contributes a large amodal-completion dataset with shadow effects, a SVD-based video-diffusion completion model, and a shape-prior injection mechanism that enables regeneration with preserved alignment. The qualitative results are visually appealing, and the runtime advantage over SDS-based baselines is compelling. However, the current evaluation does not directly measure the paper's core promise of compositional completeness in 3D: scene-level scores are computed on 2D renderings of 12 scenes, and the amodal module is validated on 2D datasets. The keystone assumption that TRELLIS generalizes from single objects to full-room isometric views is asserted without scene-level validation. These are significant gaps for a journal submission, though they appear addressable with additional experiments.

major comments (4)
  1. [Section 3.2, Scene Initialization] The first stage feeds a full-room isometric image into TRELLIS, a model trained on object-centric assets, and the manuscript supplies no scene-level validation of the resulting 3DGS scene S0. Since the subsequent EntitySeg/OmniSeg3D-GS parsing operates on S0, any instance that is fused, truncated, or omitted at this stage cannot be recovered by the later amodal completion and shape-prior stages; the decomposition-and-regeneration cascade inherits every failure of initialization. The paper should report, on a modest set of scenes, instance-level parsing quality—for example, the number of detected instances versus the expected count, per-instance 3D overlap against manually annotated Gaussians, or at least a failure-case analysis—to demonstrate that S0 is parseable into cleanly separated objects.
  2. [Section 4.2, Table 2] The amodal completion model is validated on 2D datasets (COCO-A, BSDS-A) with mIoU on amodal masks, but the paper's central claim concerns 3D instance completeness: each object in the generated scene should be an intact, manipulatable 3D entity. Table 1 only reports 2D-rendered aesthetic/CLIP/ImageReward scores, and the user study asks about matching degree and overall quality, not about object completeness. The authors should directly measure per-instance 3D completeness—for instance, by comparing the regenerated object's geometry with the initially visible region, by measuring the fraction of instances whose regenerated 3DGS covers the amodal mask footprint across multiple views, or by asking users to judge whether each object remains intact under rotation.
  3. [Section 4.1, Table 1] The comparison to GALA3D and DreamScene is under-specified: the paper states that both baselines require predefined 3D layouts, but it does not describe how these layouts were provided (e.g., from an LLM, from the same text, or from a human). If the baselines were run without their expected layout input, the comparison is biased; if layouts were supplied, the protocol should be stated. Additionally, all results derive from only 12 scenes, and no per-scene breakdown or statistical significance is reported. Given the near-ceiling user scores (2.90 out of 3.00), the advantage over baselines should be supported with confidence intervals or per-item analysis.
  4. [Section 3.4 and Figure 9] The spatial alignment claim rests on a purely qualitative ablation (Figure 9). Spatial alignment is a quantitative geometric property; the paper should report a metric such as Chamfer distance, pose error, or scale error between the regenerated instance and its original placement in S0, averaged over scenes. The hand-selected timestep range t in [0.2, 0.4] is a design choice, and its sensitivity should be examined with at least a small sweep to show the full method is not fragile to this choice.
minor comments (4)
  1. [Supplementary B.3] The sentence containing 'thn illustration of the use' should read 'an illustration of the use'; please fix this typo.
  2. [References [24] and [25]] References [24] and [25] cite the same SAM paper (Kirillov et al., 2023) and should be merged into a single reference to avoid duplication and confusion.
  3. [Equation (1)] The notation in Equation (1) does not define the ranges of L, C, and N; please add explicit definitions for these quantities in the text.
  4. [Table 3] The table header 'Datasets & Method COCO-A BSDS-A I2I I2V I2I I2V' is difficult to parse; the caption should clarify that I2I and I2V refer to image-model and video-model variants, respectively, and that COCO-A and BSDS-A are the two evaluation datasets.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: HiScene's modules are trained on independently constructed data and evaluated on external benchmarks, and no derivation step reduces to its own inputs.

full rationale

The paper's derivation chain is self-contained against external evidence. The amodal completion model is trained on a newly constructed dataset (Objaverse-filtered synthetic shadow data plus Pix2gestalt-derived pairs, Sec. 3.3 and Supp. B) and evaluated on the held-out external benchmarks COCO-A and BSDS-A (Sec. 4.2, Table 2), where the reported mIoU values are measured outputs rather than fitted inputs. The video-model-versus-image-model ablation (Sec. 4.3, Table 3) compares two models under identical data settings using independent quality metrics, so its conclusion is not baked in by construction. Scene initialization (Sec. 3.2) does invoke TRELLIS on room-level isometric views, and this is an unvalidated distributional assumption that could undermine the method, but that is a correctness or robustness risk, not circularity: the paper does not define any quantity in terms of the claimed scene-quality result, and no equation reduces to its own input. The spatial alignment stage (Sec. 3.4) uses LRM reconstruction as a conditioning prior rather than as the predicted quantity; the regenerated object is produced by the generative model with the prior as initialization, so the output is not equivalent to the input. The Limitations section (Sec. 5) states that textures have baked lighting and lack PBR materials, which is an acknowledged scope limitation, not a circular step. There are no load-bearing self-citations: the external systems used (TRELLIS, InstantMesh, OmniSeg3D-GS, SVD) are cited as prior work, not as the present authors' own unverified results, and the central claims are supported by external benchmarks, user studies, and ablations. Consequently, the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical or representational entities are introduced; the method composes existing representations (3DGS, SLAT latents, voxels). The free parameters are two hand-chosen thresholds/timesteps. The axioms are the central domain assumptions: TRELLIS handling scene-level inputs, LRM/TRELLIS latent compatibility, VLM reliability, and synthetic-to-real shadow transfer.

free parameters (2)
  • Occlusion ratio threshold τ = 0.4
    Supplementary A.3: if ρ < 0.4, amodal completion is applied; otherwise the image is discarded. Chosen by hand, no sensitivity analysis.
  • Shape prior injection timestep t = [0.2, 0.4]
    Main Sec. 3.4 and supplementary Algorithm 3: noise corresponding to an intermediate timestep t is added to the voxel latent. The value is chosen heuristically; no ablation is reported.
assumptions (4)
  • ad hoc to paper A full-room isometric view behaves like an object for TRELLIS, enabling scene-as-object generation.
    Sec. 3.2 Scene Initialization. This is the key insight but is never validated on scene-level inputs; if TRELLIS fails here, downstream parsing and regeneration break.
  • ad hoc to paper LRM geometry latents and TRELLIS structure latents are compatible enough that initializing TRELLIS sampling at t in [0.2, 0.4] with an LRM-derived voxel latent yields spatially aligned objects.
    Sec. 3.4. The prior injection is asserted without proof of latent-space compatibility.
  • domain assumption VLM-based occlusion detection and ratio estimation are sufficiently accurate for automatic pipeline decisions.
    Sec. 3.2 Occlusion Analysis and supplementary Figure 11. No quantitative evaluation of the VLM's accuracy is given.
  • domain assumption Synthetic shadows from Blender/Objaverse transfer to real occlusion scenarios.
    Supplementary B.1. The model is trained on synthetic shadow data and tested on real COCO-A/BSDS-A and in-the-wild images; the transfer assumption is implicit.

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Cite this review

Pith. "Pith review of HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation." pith.science (2026). https://pith.science/paper/73K3HPC7

@misc{pith2026250413072,
  author       = {Pith},
  title        = {Pith review of: HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/73K3HPC7}},
  note         = {Machine review of arXiv:2504.13072}
}
read the original abstract

Scene-level 3D generation represents a critical frontier in multimedia and computer graphics, yet existing approaches either suffer from limited object categories or lack editing flexibility for interactive applications. In this paper, we present HiScene, a novel hierarchical framework that bridges the gap between 2D image generation and 3D object generation and delivers high-fidelity scenes with compositional identities and aesthetic scene content. Our key insight is treating scenes as hierarchical "objects" under isometric views, where a room functions as a complex object that can be further decomposed into manipulatable items. This hierarchical approach enables us to generate 3D content that aligns with 2D representations while maintaining compositional structure. To ensure completeness and spatial alignment of each decomposed instance, we develop a video-diffusion-based amodal completion technique that effectively handles occlusions and shadows between objects, and introduce shape prior injection to ensure spatial coherence within the scene. Experimental results demonstrate that our method produces more natural object arrangements and complete object instances suitable for interactive applications, while maintaining physical plausibility and alignment with user inputs.

Figures

Figures reproduced from arXiv: 2504.13072 by the authors.

Figure 1
Figure 1. HiScene allows users to generate scene-level 3D assets with natural layout and appealing looking, while delivering compositional items for versatile applications such as interactive editing and simulation. Abstract Scene-level 3D generation represents a critical frontier in multimedia and computer graphics, yet existing approaches either suffer from limited object categories or lack edit￾ing flexibility for interact… view at source ↗
Figure 2
Figure 2. Overview of HiScene. Our hierarchical framework generates 3D scenes with compositional identities through three main stages. First, we create a 3D scene from a generated isometric view. Next, we perform scene parsing to obtain precise object segmentation, followed by multi-view rendering and detailed occlusion analysis for each identified instance. Finally, we apply our video-diffusion-based amodal completion to gen… view at source ↗
Figure 3
Figure 3. Comparison of perspective view and isometric view of a [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: We present an data curation example of amodal com [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: An illustration of Spatial Aligned Generation. We use sparse-view LRM to initialize spatial aligned shape prior (voxel latent), and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: We compare the Interactive Scene 3D generation with GALA3D and DreamScene. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: In-the-wild Amodal Completion and Segmentation. Input Image &. Mask w/o Completion w/o Shadow Data Full Method [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: We analyze the effectiveness of Spatial Aligned Genera [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: We provide examples of predefined viewpoints. [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: We provide an example of using VLM to determine [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: (a) Visualization of the Rigid Body Dynamics (RBD) process; (b) and (c) illustrate the shadow effects under different lighting [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: An illustration of the user study interface. [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: We show examples of our synthetic dataset. [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: More examples of generated scenes. All prompts have a fixed prefix [PITH_FULL_IMAGE:figures/full_fig_p019_15.png]
Figure 16
Figure 16. Figure 16: More examples of amodal completion. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]

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

  1. 3D Scene Generation: A Survey

    cs.CV 2025-05 conditional

    The paper surveys 3D scene generation and organizes methods into four paradigms, with datasets, evaluation metrics, applications, and future directions.

Reference graph

Works this paper leans on

94 extracted references · 40 canonical work pages · cited by 1 Pith paper

  1. [1]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 14

    arXiv 2023

  2. [2]

    Blended diffusion for text-driven editing of natural images

    Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208–18218, 2022. 3

    2022

  3. [3]

    Blended latent diffusion

    Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM transactions on graphics (TOG), 42(4): 1–11, 2023. 3

    2023

  4. [4]

    Iron- depth: Iterative refinement of single-view depth using surface normal and its uncertainty

    Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Iron- depth: Iterative refinement of single-view depth using surface normal and its uncertainty. arXiv preprint arXiv:2210.03676,

    arXiv

  5. [5]

    Image inpainting

    Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. Image inpainting. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 417–424, 2000. 3

    2000

  6. [6]

    Zoedepth: Zero-shot trans- fer by combining relative and metric depth

    Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias M ¨uller. Zoedepth: Zero-shot trans- fer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 3

    arXiv 2023

  7. [7]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    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. 5, 16

    arXiv 2023

  8. [8]

    Scenefactor: Factored latent 3d diffusion for control- lable 3d scene generation

    Alexey Bokhovkin, Quan Meng, Shubham Tulsiani, and An- gela Dai. Scenefactor: Factored latent 3d diffusion for control- lable 3d scene generation. arXiv preprint arXiv:2412.01801,

    arXiv

Show all 94 references
  1. [9]

    Fan- tasia3d: Disentangling geometry and appearance for high- quality text-to-3d content creation

    Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fan- tasia3d: Disentangling geometry and appearance for high- quality text-to-3d content creation. In Proceedings of the IEEE/CVF international conference on computer vision , pages 22246–22256, 2023. 3

    2023

  2. [10]

    Region-aware text-to-image generation via hard binding and soft refinement

    Zhennan Chen, Yajie Li, Haofan Wang, Zhibo Chen, Zhengkai Jiang, Jun Li, Qian Wang, Jian Yang, and Ying Tai. Region-aware text-to-image generation via hard binding and soft refinement. arXiv preprint arXiv:2411.06558, 2024. 3

    2024 arXiv

  3. [11]

    3dtopia-xl: Scaling high-quality 3d asset generation via primitive diffusion

    Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong, Ziang Cao, Fangzhou Hong, Yushi Lan, Tengfei Wang, Haozhe Xie, Tong Wu, Shunsuke Saito, et al. 3dtopia-xl: Scaling high-quality 3d asset generation via primitive diffusion. arXiv preprint arXiv:2409.12957, 2024. 3

    2024 arXiv

  4. [12]

    Click-gaussian: Interactive segmenta- tion to any 3d gaussians

    Seokhun Choi, Hyeonseop Song, Jaechul Kim, Taehyeong Kim, and Hoseok Do. Click-gaussian: Interactive segmenta- tion to any 3d gaussians. In European Conference on Com- puter Vision, pages 289–305. Springer, 2024. 14

    2024

  5. [13]

    Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes

    Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 2, 3

    2023 arXiv

  6. [14]

    Re- gion filling and object removal by exemplar-based image inpainting

    Antonio Criminisi, Patrick P´erez, and Kentaro Toyama. Re- gion filling and object removal by exemplar-based image inpainting. IEEE Transactions on image processing, 13(9): 1200–1212, 2004. 3

    2004

  7. [15]

    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

  8. [16]

    Text2room: Extracting textured 3d meshes from 2d text-to-image models

    Lukas H ¨ollein, Ang Cao, Andrew Owens, Justin Johnson, and Matthias Nießner. Text2room: Extracting textured 3d meshes from 2d text-to-image models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7909–7920, 2023. 2, 3

    2023

  9. [17]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 3

    2023 arXiv

  10. [18]

    Tracking any object amodally

    Cheng-Yen Hsieh, Tarasha Khurana, Achal Dave, and Deva Ramanan. Tracking any object amodally. CoRR, 2023. 3

    2023

  11. [19]

    Smarteraser: Remove anything from images using masked- region guidance

    Longtao Jiang, Zhendong Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Lei Shi, Dong Chen, and Houqiang Li. Smarteraser: Remove anything from images using masked- region guidance. arXiv preprint arXiv:2501.08279 , 2025. 3

    2025 arXiv

  12. [20]

    Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion

    Xuan Ju, Xian Liu, Xintao Wang, Yuxuan Bian, Ying Shan, and Qiang Xu. Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion. In European Conference on Computer Vision, pages 150–168. Springer,

  13. [21]

    Amodal completion and size constancy in natural scenes

    Abhishek Kar, Shubham Tulsiani, Joao Carreira, and Jitendra Malik. Amodal completion and size constancy in natural scenes. In Proceedings of the IEEE international conference on computer vision, pages 127–135, 2015. 3

    2015

  14. [22]

    Deep occlusion- aware instance segmentation with overlapping bilayers

    Lei Ke, Yu-Wing Tai, and Chi-Keung Tang. Deep occlusion- aware instance segmentation with overlapping bilayers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4019–4028, 2021. 3

    2021

  15. [23]

    3d gaussian splatting for real-time radiance field rendering

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

    2023

  16. [24]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pages 4015–4026, 2023. 5, 15

    2023

  17. [25]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pages 4015–4026, 2023. 5, 7

    2023

  18. [26]

    Open3dsg: Open-vocabulary 3d scene graphs from point clouds with queryable objects and open-set relationships

    Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pedro Hermosilla, and Timo Ropinski. Open3dsg: Open-vocabulary 3d scene graphs from point clouds with queryable objects and open-set relationships. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog...

    2024

  19. [27]

    Black Forest Labs. Flux. https://github.com/ black-forest-labs/flux, 2024. 17

    2024

  20. [28]

    Gaussiananything: Interactive point cloud latent diffusion for 3d generation

    Yushi Lan, Shangchen Zhou, Zhaoyang Lyu, Fangzhou Hong, Shuai Yang, Bo Dai, Xingang Pan, and Chen Change Loy. Gaussiananything: Interactive point cloud latent diffusion for 3d generation. arXiv preprint arXiv:2411.08033, 2024. 3

    2024 arXiv

  21. [29]

    Dreamscene: 3d gaussian-based text-to-3d scene generation via formation pattern sampling

    Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu, Yong Liao, Lin Wang, Lik-hang Lee, and Peng Yuan Zhou. Dreamscene: 3d gaussian-based text-to-3d scene generation via formation pattern sampling. In European Conference on Computer Vision, pages 214–230. Springer, 2024. 3, 6, 7

    2024

  22. [30]

    Prototypical contrastive learning of unsupervised representa- tions

    Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. Prototypical contrastive learning of unsupervised representa- tions. arXiv preprint arXiv:2005.04966, 2020. 14

    2005 arXiv

  23. [31]

    Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model

    Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 3

    2023 arXiv

  24. [32]

    Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner

    Weiyu Li, Jiarui Liu, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, and Xiaoxiao Long. Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner. arXiv preprint arXiv:2405.14979, 2024. 3

    2024 arXiv

  25. [33]

    Discene: Object decoupling and interaction modeling for complex scene generation

    Xiao-Lei Li, Haodong Li, Hao-Xiang Chen, Tai-Jiang Mu, and Shi-Min Hu. Discene: Object decoupling and interaction modeling for complex scene generation. In SIGGRAPH Asia 2024 Conference Papers, pages 1–12, 2024. 3

    2024

  26. [34]

    Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching

    Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6517–6526, 2024. 3

    2024

  27. [35]

    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. 3

    2024 arXiv

  28. [36]

    Magic3d: High-resolution text- to-3d content creation

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text- to-3d content creation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognit...

    2023

  29. [37]

    Variational amodal object completion

    Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, and Sanja Fidler. Variational amodal object completion. Ad- vances in Neural Information Processing Systems, 33:16246– 16257, 2020. 3

    2020

  30. [38]

    Image inpainting via tractable steering of diffusion models

    Anji Liu, Mathias Niepert, and Guy Van den Broeck. Image inpainting via tractable steering of diffusion models. arXiv preprint arXiv:2401.03349, 2023. 3

    2023 arXiv

  31. [39]

    Pd-gan: Probabilistic diverse gan for image inpainting

    Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, and Jing Liao. Pd-gan: Probabilistic diverse gan for image inpainting. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 9371–9381, 2021. 3

    2021

  32. [40]

    One-2-3-45++: Fast single image to 3d ob- jects with consistent multi-view generation and 3d diffusion

    Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d ob- jects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF conference on co...

    2024

  33. [41]

    Zero-1-to-3: Zero-shot one image to 3d object

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl V ondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision , pages 9298– 9309, 2023. 3

    2023

  34. [42]

    Syncdreamer: Gener- ating multiview-consistent images from a single-view image

    Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Gener- ating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 3

    2023 arXiv

  35. [43]

    Wonder3d: Single im- age to 3d using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single im- age to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF conference on computer vision and patt...

    2024

  36. [44]

    A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

    David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings eighth IEEE international conference on computer vision. IC...

    2001

  37. [45]

    Lt3sd: Latent trees for 3d scene diffusion

    Quan Meng, Lei Li, Matthias Nießner, and Angela Dai. Lt3sd: Latent trees for 3d scene diffusion. arXiv preprint arXiv:2409.08215, 2024. 3

    2024 arXiv

  38. [46]

    Latent-nerf for shape-guided generation of 3d shapes and textures

    Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12663–12673, 2023. 3

    2023

  39. [47]

    Ava: A large-scale database for aesthetic visual analysis

    Naila Murray, Luca Marchesotti, and Florent Perronnin. Ava: A large-scale database for aesthetic visual analysis. In 2012 IEEE conference on computer vision and pattern recognition, pages 2408–2415. IEEE, 2012. 7, 8

    2012

  40. [48]

    Sceneteller: Language-to-3d scene generation

    Bas ¸ak Melis ¨Ocal, Maxim Tatarchenko, Sezer Karao˘glu, and Theo Gevers. Sceneteller: Language-to-3d scene generation. In European Conference on Computer Vision, pages 362–378. Springer, 2024. 3

    2024

  41. [49]

    pix2gestalt: Amodal segmentation by synthesizing wholes

    Ege Ozguroglu, Ruoshi Liu, D´ıdac Sur´ıs, Dian Chen, Achal Dave, Pavel Tokmakov, and Carl V ondrick. pix2gestalt: Amodal segmentation by synthesizing wholes. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3931–3940. IEEE Computer So- ciety...

    2024

  42. [50]

    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,

  43. [51]

    Gener- ating diverse structure for image inpainting with hierarchical vq-vae

    Jialun Peng, Dong Liu, Songcen Xu, and Houqiang Li. Gener- ating diverse structure for image inpainting with hierarchical vq-vae. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 10775–10784,

  44. [52]

    11 Sdxl: Improving latent diffusion models for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M¨uller, Joe Penna, and Robin Rombach. 11 Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 7

    2023 arXiv

  45. [53]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3

    2022 arXiv

  46. [54]

    Amodal instance segmentation with kins dataset

    Lu Qi, Li Jiang, Shu Liu, Xiaoyong Shen, and Jiaya Jia. Amodal instance segmentation with kins dataset. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3014–3023, 2019. 3

    2019

  47. [55]

    High-quality entity segmentation

    Lu Qi, Jason Kuen, Weidong Guo, Tiancheng Shen, Jiuxiang Gu, Jiaya Jia, Zhe Lin, and Ming-Hsuan Yang. High-quality entity segmentation. arXiv preprint arXiv:2211.05776, 2022. 5, 14

    2022 arXiv

  48. [56]

    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

  49. [57]

    Walt: Watch and learn 2d amodal representation from time-lapse imagery

    N Dinesh Reddy, Robert Tamburo, and Srinivasa G Narasimhan. Walt: Watch and learn 2d amodal representation from time-lapse imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9356–9366, 2022. 3

    2022

  50. [58]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 3

    2022

  51. [59]

    Zero123++: a single image to consistent multi-view diffusion base model

    Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 3

    2023 arXiv

  52. [60]

    Mvdream: Multi-view diffusion for 3d gener- ation

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d gener- ation. arXiv preprint arXiv:2308.16512, 2023. 3

    2023 arXiv

  53. [61]

    Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation. arXiv preprint arXiv:2309.16653,

    arXiv

  54. [62]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation

    Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision, pages 1–18. Springer, 2024. 3

    2024

  55. [63]

    Diffuscene: Denoising diffusion models for generative indoor scene synthesis

    Jiapeng Tang, Yinyu Nie, Lev Markhasin, Angela Dai, Justus Thies, and Matthias Nießner. Diffuscene: Denoising diffusion models for generative indoor scene synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20507–20518, 2024. 3

    2024

  56. [64]

    Emergent correspondence from image diffusion

    Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems, 36:1363–1389, 2023. 3

    2023

  57. [65]

    Score jacobian chaining: Lifting pre- trained 2d diffusion models for 3d generation

    Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pre- trained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12619–12629, 2023. 3

    2023

  58. [66]

    Architect: Generating vivid and interactive 3d scenes with hierarchical 2d inpainting

    Yian Wang, Xiaowen Qiu, Jiageng Liu, Zhehuan Chen, Jiting Cai, Yufei Wang, Tsun-Hsuan Johnson Wang, Zhou Xian, and Chuang Gan. Architect: Generating vivid and interactive 3d scenes with hierarchical 2d inpainting. Advances in Neural Information Processing Systems, 37:67575–676...

    2024

  59. [67]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion. Advances in Neural Information Processing Systems , 36:8406–8441, 2023. 3

    2023

  60. [68]

    Q-align: Teaching lmms for vi- sual scoring via discrete text-defined levels

    Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching lmms for vi- sual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090, 2023. 7, 8

    2023 arXiv

  61. [69]

    Blockfusion: Expandable 3d scene gener- ation using latent tri-plane extrapolation

    Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, et al. Blockfusion: Expandable 3d scene gener- ation using latent tri-plane extrapolation. ACM Transactions on Graphics (TOG), 43(4):1–17, 2024. 3

    2024

  62. [70]

    Structured 3d latents for scalable and versatile 3d generation

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.01506, 2024. 2, 3, 4, 6

    2024 arXiv

  63. [71]

    Sparp: Fast 3d object reconstruction and pose estimation from sparse views

    Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su, and Minghua Liu. Sparp: Fast 3d object reconstruction and pose estimation from sparse views. In European Con- ference on Computer Vision, pages 143–163. Springer, 2024. 3

    2024

  64. [72]

    Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models

    Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models. arXiv preprint arXiv:2404.07191 ,

    arXiv

  65. [73]

    Amodal com- pletion via progressive mixed context diffusion

    Katherine Xu, Lingzhi Zhang, and Jianbo Shi. Amodal com- pletion via progressive mixed context diffusion. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9099–9109, 2024. 3

    2024

  66. [74]

    Grm: Large gaussian reconstruction model for efficient 3d recon- struction and generation

    Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d recon- struction and generation. In European Conference on Com- puter Vision, pages 1–20. Springer, 2024. 3

    2024

  67. [75]

    Scene synthesis via uncertainty-driven attribute syn- chronization

    Haitao Yang, Zaiwei Zhang, Siming Yan, Haibin Huang, Chongyang Ma, Yi Zheng, Chandrajit Bajaj, and Qixing Huang. Scene synthesis via uncertainty-driven attribute syn- chronization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5630–5640, 2021. 3

    2021

  68. [76]

    Gaus- sian grouping: Segment and edit anything in 3d scenes

    Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke. Gaus- sian grouping: Segment and edit anything in 3d scenes. In European Conference on Computer Vision, pages 162–179. Springer, 2024. 14

    2024

  69. [77]

    Gaussiandreamer: Fast generation from text to 3d gaus- sians by bridging 2d and 3d diffusion models

    Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaus- sians by bridging 2d and 3d diffusion models. In Proceedings 12 of the IEEE/CVF Conference on Computer Vis...

    2024

  70. [78]

    Omniseg3d: Omniversal 3d segmentation via hierarchical contrastive learning

    Haiyang Ying, Yixuan Yin, Jinzhi Zhang, Fan Wang, Tao Yu, Ruqi Huang, and Lu Fang. Omniseg3d: Omniversal 3d segmentation via hierarchical contrastive learning. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20612–20622, 2024. 2, 5, 14

    2024

  71. [79]

    Wonderworld: Interactive 3d scene generation from a single image

    Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3d scene generation from a single image. arXiv preprint arXiv:2406.09394, 2024. 3

    2024 arXiv

  72. [80]

    Wonderjourney: Going from anywhere to everywhere

    Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T Freeman, Forrester Cole, De- qing Sun, Noah Snavely, Jiajun Wu, et al. Wonderjourney: Going from anywhere to everywhere. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R...

    2024

  73. [81]

    Commonscenes: Generating commonsense 3d indoor scenes with scene graph diffusion

    Guangyao Zhai, Evin Pınar ¨Ornek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, and Benjamin Busam. Commonscenes: Generating commonsense 3d indoor scenes with scene graph diffusion. Advances in Neural Information Processing Systems, 36:30026–30038, 2023. 3

    2023

  74. [82]

    Echoscene: Indoor scene generation via information echo over scene graph diffusion

    Guangyao Zhai, Evin Pınar ¨Ornek, Dave Zhenyu Chen, Ruo- tong Liao, Yan Di, Nassir Navab, Federico Tombari, and Benjamin Busam. Echoscene: Indoor scene generation via information echo over scene graph diffusion. In European Conference on Computer Vision, pages 167–184. Springer,

  75. [83]

    Amodal ground truth and completion in the wild

    Guanqi Zhan, Chuanxia Zheng, Weidi Xie, and Andrew Zis- serman. Amodal ground truth and completion in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 28003–28013, 2024. 3

    2024

  76. [84]

    Self-supervised scene de-occlusion

    Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, and Chen Change Loy. Self-supervised scene de-occlusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3784–3792, 2020. 7

    2020

  77. [85]

    3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models

    Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 42(4):1–16, 2023. 3

    2023

  78. [86]

    Text2nerf: Text-driven 3d scene generation with neu- ral radiance fields

    Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. Text2nerf: Text-driven 3d scene generation with neu- ral radiance fields. IEEE Transactions on Visualization and Computer Graphics, 30(12):7749–7762, 2024. 3

    2024

  79. [87]

    Clay: A controllable large-scale generative model for creating high- quality 3d assets

    Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high- quality 3d assets. ACM Transactions on Graphics (TOG), 43 (4):1–20, 2024. 3

    2024

  80. [88]

    Towards text-guided 3d scene composition

    Qihang Zhang, Chaoyang Wang, Aliaksandr Siarohin, Peiye Zhuang, Yinghao Xu, Ceyuan Yang, Dahua Lin, Bolei Zhou, Sergey Tulyakov, and Hsin-Ying Lee. Towards text-guided 3d scene composition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , ...

    2024

  81. [89]

    Large scale image comple- tion via co-modulated generative adversarial networks

    Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I Chang, and Yan Xu. Large scale image comple- tion via co-modulated generative adversarial networks. arXiv preprint arXiv:2103.10428, 2021. 3

    2021 arXiv

  82. [90]

    Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation

    Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang, Pei Cheng, Bin Fu, Tao Chen, Gang Yu, and Shenghua Gao. Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation. Advances in neural information processing systems, 36:73969–73982,

  83. [91]

    Hunyuan3d 2.0: Scaling diffusion mod- els for high resolution textured 3d assets generation

    Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion mod- els for high resolution textured 3d assets generation. arXiv preprint arXiv:2501.12202, 2025. 3

    2025 arXiv

  84. [92]

    Dreamscene360: Unconstrained text-to-3d scene generation with panoramic gaussian splatting

    Shijie Zhou, Zhiwen Fan, Dejia Xu, Haoran Chang, Pradyumna Chari, Tejas Bharadwaj, Suya You, Zhangyang Wang, and Achuta Kadambi. Dreamscene360: Unconstrained text-to-3d scene generation with panoramic gaussian splatting. In European Conference on Computer Vision, pages 324–342...

    2024

  85. [93]

    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. 3, 6, 7

    2024 arXiv

  86. [94]

    1": "A wooden nightstand with a lamp

    Yan Zhu, Yuandong Tian, Dimitris Metaxas, and Piotr Doll´ar. Semantic amodal segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1464–1472, 2017. 7 13 HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation Supp...

    2017

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.