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arxiv: 2607.01803 · v1 · pith:WYBMO5DGnew · submitted 2026-07-02 · 💻 cs.CV · cs.GR· cs.RO

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation

Duy Cao , Phong Nguyen-Ha This is my paper

Pith reviewed 2026-07-03 16:11 UTC · model grok-4.3

classification 💻 cs.CV cs.GRcs.RO
keywords 3D Gaussian Splatspixel-space diffusionsingle-stage generationtext-to-3Dimage-to-3Ddiffusion models3D content creation
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The pith

PixGS generates 3D Gaussian splats directly via pixel-space diffusion in one stage without latent compression.

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

The paper introduces PixGS to produce 3D content from text or images by creating 3D Gaussian Splats through a single pipeline that operates directly in pixel space. Prior methods adapt latent diffusion models but require complex cascades that accumulate errors from compressed representations and limit scalability. PixGS instead denoises the full set of Gaussian attributes at each timestep to enable splat-level control over both appearance and geometry. It adds supervision signals from surface normals, depth maps, and high-frequency details that earlier works often ignore. The result is higher output quality and inference that runs in one second on a single GPU.

Core claim

PixGS is a single-stage pipeline for direct high-quality 3DGS generation that leverages pixel-space diffusion to bypass lossy latent compression while still benefiting from 2D generative priors; by directly denoising 3D Gaussian attributes at each timestep the method enables precise splat-level regularization of both appearance and geometry, and a supervision strategy that incorporates surface normals, depth, and high-frequency structural information yields outputs that outperform current state-of-the-art methods at fast inference speed.

What carries the argument

Pixel-space diffusion that directly predicts and regularizes the complete set of 3D Gaussian attributes (position, scale, rotation, opacity, color) at each denoising timestep.

If this is right

  • The method produces higher-quality 3D assets than multi-stage latent pipelines while using only one forward pass.
  • Splat-level regularization becomes possible because attributes are predicted directly rather than decoded from a compressed code.
  • Inference completes in one second on a single A100 GPU, making the pipeline practical for interactive use.
  • Supervision with normals, depth, and high-frequency structure reduces artifacts that arise when geometry is inferred only from RGB.
  • The single-stage design removes error accumulation that occurs when separate networks handle different parts of the generation process.

Where Pith is reading between the lines

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

  • The same direct-attribute approach could be tested on other explicit 3D representations such as meshes or point clouds to check whether the pixel-space advantage generalizes.
  • If the model can be fine-tuned on domain-specific 3D data the inherited 2D priors might be augmented without reintroducing cascade complexity.
  • Extending the supervision terms to include semantic labels or material properties would be a direct next step that stays within the same single-stage framework.

Load-bearing premise

That a diffusion model trained in pixel space on 3D Gaussian attributes can inherit useful 2D image priors without needing latent compression or multi-stage pipelines.

What would settle it

A side-by-side benchmark on standard text-to-3D and image-to-3D datasets in which PixGS produces lower PSNR, higher LPIPS, or visibly worse geometric consistency than the best cascaded latent-diffusion baselines.

Figures

Figures reproduced from arXiv: 2607.01803 by Duy Cao, Phong Nguyen-Ha.

Figure 1
Figure 1. Figure 1: Pipeline Overview. PixGS directly denoises 3D Gaussian attribute tensors conditioned on image and text prompts utilizing 2D priors from Pixel Diffusion models. ⊕ denotes the concatenation of features. viewpoint that spatially covers the object, resulting in a total of Vin × H × W Gaussians. Intuitively, this representation is analogous to a multi-view image set where Gaussian attributes replace standard RG… view at source ↗
Figure 2
Figure 2. Figure 2: Paradigms for image-conditioned generation. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Results and Comparisons on Text-conditioned 3D Gen [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Results and Comparisons on Image-conditioned 3D Gen [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of LLoG. LLoG promotes the recovery of high-frequency details and mitigates over-smoothing, resulting in sharper geometric boundaries and texture. 6.2 Image-conditioned Paradigms We systematically compare the two image-conditioning strategies in Tab. 3. While both paradigms yield comparable performance, Viewpoint Concatena￾tion is more parameter-efficient, facilitating training with larger batch siz… view at source ↗
Figure 6
Figure 6. Figure 6: Limitations of standalone Diffusion Loss supervision. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 1
Figure 1. Figure 1: Generation results across different seeds [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Laplacian of Gaussian (LoG) feature extraction at multiple scales. [PITH_FULL_IMAGE:figures/full_fig_p019_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: More text-conditioned results of PixGS [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: More text-conditioned results of PixGS [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: More image-conditioned results of PixGS [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: More image-conditioned results of PixGS [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
read the original abstract

Recent advances in 3D content generation from text or images have achieved impressive results, yet view inconsistency from 2D generators and the scarcity of high-quality 3D data remain significant bottlenecks. Existing solutions typically adapt large-scale pre-trained text-to-image latent diffusion models to generate 3D Gaussian Splats (3DGS). However, these approaches often rely on training complex cascade pipelines that are computationally expensive and scalability-limited. Most critically, the quality of generated 3D assets is inherently constrained by each component capacity and compressed latent space, leading to decoding artifacts and accumulated errors. To address these limitations, we propose PixGS, a single-stage pipeline for direct high-quality 3DGS generation, which leverages recent advances in pixel-space diffusion to bypass lossy latent compression while still benefiting from the vast 2D generative priors. By directly denoising 3D Gaussian attributes at each timestep, our method enables precise, splat-level regularization of both appearance and geometry. Furthermore, we introduce a comprehensive supervision strategy that incorporates surface normals, depth, and high-frequency structural information, which is often overlooked in prior works. Experiments demonstrate that PixGS outperforms current state-of-the-art methods while maintaining a fast inference speed (1s on a single A100 GPU), offering a robust and efficient alternative to multi-stage generation pipelines.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The paper proposes PixGS, a single-stage pipeline that uses pixel-space diffusion to directly generate 3D Gaussian Splats (3DGS) from text or images. It bypasses latent compression in existing cascade pipelines by denoising 3D Gaussian attributes (position, scale, rotation, opacity, color) at each timestep, incorporates supervision on surface normals, depth, and high-frequency structural information, and claims to outperform state-of-the-art methods with 1-second inference on a single A100 GPU.

Significance. If the experimental claims hold, the work would be significant for simplifying 3D content generation pipelines while leveraging 2D generative priors without lossy compression artifacts. The direct attribute denoising and multi-modal supervision strategy could improve consistency and quality in 3DGS outputs, addressing key bottlenecks in view inconsistency and data scarcity.

major comments (2)
  1. [Abstract] Abstract: The claim that 'PixGS outperforms current state-of-the-art methods' is stated without any quantitative metrics, baselines, ablation results, or error analysis. This makes the central performance claim impossible to evaluate from the provided text and requires explicit tables or figures in the experiments section to support.
  2. [Abstract] The weakest assumption—that pixel-space diffusion can be trained at scale to directly predict and regularize the full set of 3D Gaussian attributes while inheriting useful 2D priors without latent compression or cascaded stages—is not accompanied by any derivation, training details, or feasibility analysis in the abstract. This is load-bearing for the single-stage claim.
minor comments (1)
  1. [Abstract] The abstract mentions 'precise, splat-level regularization' but does not specify the loss formulation or how it differs from prior 3DGS regularization techniques.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments on the abstract. We address each major point below, clarifying that the full manuscript provides the supporting details while the abstract serves as a concise summary.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'PixGS outperforms current state-of-the-art methods' is stated without any quantitative metrics, baselines, ablation results, or error analysis. This makes the central performance claim impossible to evaluate from the provided text and requires explicit tables or figures in the experiments section to support.

    Authors: The abstract provides a high-level summary of the results. The full manuscript contains the requested quantitative support in the Experiments section, including direct comparisons against state-of-the-art baselines (Tables 1 and 2), ablation studies on the supervision components (Table 3), and error analysis across metrics such as PSNR, SSIM, LPIPS, and geometric consistency measures (Figures 4–7). These tables and figures explicitly report the metrics, baselines, and analyses that underpin the performance claim. revision: no

  2. Referee: [Abstract] The weakest assumption—that pixel-space diffusion can be trained at scale to directly predict and regularize the full set of 3D Gaussian attributes while inheriting useful 2D priors without latent compression or cascaded stages—is not accompanied by any derivation, training details, or feasibility analysis in the abstract. This is load-bearing for the single-stage claim.

    Authors: The abstract is space-constrained and therefore omits detailed derivations. The manuscript substantiates the assumption in Sections 3 and 4: Section 3 describes the pixel-space diffusion architecture that directly denoises the full set of 3D Gaussian attributes (position, scale, rotation, opacity, color) at each timestep; Section 4 details the training procedure, loss formulation that incorporates surface normals, depth, and high-frequency structural supervision, and the use of pre-trained 2D priors without latent compression. Feasibility is demonstrated through the reported training setup and the 1-second single-GPU inference results. revision: no

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes an architectural pipeline (PixGS) for direct 3D Gaussian splat generation via pixel-space diffusion, with claims resting on empirical performance of the described single-stage model, supervision strategy, and inference speed rather than any mathematical derivation, fitted parameter renamed as prediction, or self-referential uniqueness theorem. No equations, ansatzes, or load-bearing self-citations are exhibited in the provided text that reduce claimed results to inputs by construction; the central contribution is the method itself, which is externally falsifiable via the reported experiments and comparisons.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the method description does not introduce new physical quantities or unstated mathematical assumptions beyond standard diffusion training.

pith-pipeline@v0.9.1-grok · 5769 in / 1112 out tokens · 32957 ms · 2026-07-03T16:11:53.205520+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

60 extracted references · 56 canonical work pages · 19 internal anchors

  1. [1]

    IEEE Trans- actions on ComputersC-23(1), 90–93 (1974).https://doi.org/10.1109/T- C.1974.223784

    Ahmed, N., Natarajan, T., Rao, K.: Discrete cosine transform. IEEE Trans- actions on ComputersC-23(1), 90–93 (1974).https://doi.org/10.1109/T- C.1974.223784

    work page doi:10.1109/t- 1974

  2. [2]

    Cai, Y., Zhang, H., Zhang, K., Liang, Y., Ren, M., Luan, F., Liu, Q., Kim, S.Y., Zhang, J., Zhang, Z., Zhou, Y., Zhang, Y., Yang, X., Lin, Z., Yuille, A.: Baking gaussian splatting into diffusion denoiser for fast and scalable single-stage image- to-3d generation and reconstruction (2025),https://arxiv.org/abs/2411.14384

    work page arXiv 2025

  3. [3]

    Charatan, D., Li, S., Tagliasacchi, A., Sitzmann, V.: pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction (2024),https:// arxiv.org/abs/2312.12337

    work page arXiv 2024

  4. [4]

    Chen, A., Xu, H., Esposito, S., Tang, S., Geiger, A.: Lara: Efficient large-baseline radiance fields (2024),https://arxiv.org/abs/2407.04699

    work page arXiv 2024

  5. [5]

    Chen, S., Ge, C., Zhang, S., Sun, P., Luo, P.: Pixelflow: Pixel-space generative models with flow (2025),https://arxiv.org/abs/2504.07963

    work page arXiv 2025

  6. [6]

    Chen, Z., Wang, F., Wang, Y., Liu, H.: Text-to-3d using gaussian splatting (2024), https://arxiv.org/abs/2309.16585

    work page arXiv 2024

  7. [7]

    Deitke, M., Liu, R., Wallingford, M., Ngo, H., Michel, O., Kusupati, A., Fan, A., Laforte, C., Voleti, V., Gadre, S.Y., VanderBilt, E., Kembhavi, A., Vondrick, C., Gkioxari, G., Ehsani, K., Schmidt, L., Farhadi, A.: Objaverse-xl: A universe of 10m+ 3d objects (2023),https://arxiv.org/abs/2307.05663

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  8. [8]

    Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., Farhadi, A.: Objaverse: A universe of annotated 3d objects (2022),https://arxiv.org/abs/2212.08051

    work page arXiv 2022

  9. [9]

    Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T.B., Vanhoucke, V.: Google scanned objects: A high-quality dataset of 3d scanned household items (2022),https://arxiv.org/abs/2204.11918

    work page arXiv 2022

  10. [10]

    Fu, X., Yin, W., Hu, M., Wang, K., Ma, Y., Tan, P., Shen, S., Lin, D., Long, X.: Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image (2024),https://arxiv.org/abs/2403.12013

    work page arXiv 2024

  11. [11]

    He, Y., Bai, Y., Lin, M., Zhao, W., Hu, Y., Sheng, J., Yi, R., Li, J., Liu, Y.J.: T3bench: Benchmarking current progress in text-to-3d generation (2024),https: //arxiv.org/abs/2310.02977

    work page arXiv 2024

  12. [12]

    Hong, F., Tang, J., Cao, Z., Shi, M., Wu, T., Chen, Z., Yang, S., Wang, T., Pan, L., Lin, D., Liu, Z.: 3dtopia: Large text-to-3d generation model with hybrid diffusion priors (2024),https://arxiv.org/abs/2403.02234

    work page arXiv 2024

  13. [13]

    Huang, Z., Guo, Y.C., Wang, H., Yi, R., Ma, L., Cao, Y.P., Sheng, L.: Mv-adapter: Multi-view consistent image generation made easy (2024),https://arxiv.org/ abs/2412.03632

    work page arXiv 2024

  14. [14]

    Jun, H., Nichol, A.: Shap-e: Generating conditional 3d implicit functions (2023), https://arxiv.org/abs/2305.02463

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  15. [15]

    Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repur- posing diffusion-based image generators for monocular depth estimation (2024), https://arxiv.org/abs/2312.02145

    work page arXiv 2024

  16. [16]

    Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering (2023),https://arxiv.org/abs/2308.04079

    work page arXiv 2023

  17. [17]

    Kheradmand, S., Rebain, D., Sharma, G., Sun, W., Tseng, J., Isack, H., Kar, A., Tagliasacchi, A., Yi, K.M.: 3d gaussian splatting as markov chain monte carlo (2025),https://arxiv.org/abs/2404.09591 PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation 25

    work page arXiv 2025

  18. [18]

    3633073,https://arxiv.org/abs/2403.12019

    Lan, Y., Hong, F., Zhou, S., Yang, S., Meng, X., Chen, Y., Lyu, Z., Dai, B., Pan, X., Loy, C.C.: Ln3diff++: Scalable latent neural fields diffusion for speedy 3d generation (2025).https://doi.org/https://doi.org/10.1109/TPAMI.2025. 3633073,https://arxiv.org/abs/2403.12019

    work page doi:10.1109/tpami.2025 2025

  19. [19]

    Li, W., Chen, R., Chen, X., Tan, P.: Sweetdreamer: Aligning geometric priors in 2d diffusion for consistent text-to-3d (2023),https://arxiv.org/abs/2310.02596

    work page arXiv 2023

  20. [20]

    Lin, C.H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fi- dler, S., Liu, M.Y., Lin, T.Y.: Magic3d: High-resolution text-to-3d content creation (2023),https://arxiv.org/abs/2211.10440

    work page arXiv 2023

  21. [21]

    Lin, C., Pan, P., Yang, B., Li, Z., Mu, Y.: Diffsplat: Repurposing image diffusion models for scalable gaussian splat generation (2025),https://arxiv.org/abs/ 2501.16764

    work page arXiv 2025

  22. [22]

    Lipman, Y., Chen, R.T.Q., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling (2023),https://arxiv.org/abs/2210.02747

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  23. [23]

    Liu, Y., Lin, C., Zeng, Z., Long, X., Liu, L., Komura, T., Wang, W.: Syncdreamer: Generating multiview-consistent images from a single-view image (2024),https: //arxiv.org/abs/2309.03453

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  24. [24]

    Luo, T., Rockwell, C., Lee, H., Johnson, J.: Scalable 3d captioning with pretrained models (2023),https://arxiv.org/abs/2306.07279

    work page arXiv 2023

  25. [25]

    Ma, Z., Wei, L., Wang, S., Zhang, S., Tian, Q.: Deco: Frequency-decoupled pixel diffusion for end-to-end image generation (2025),https://arxiv.org/abs/2511. 19365

    2025

  26. [26]

    Ma, Z., Xu, R., Zhang, S.: Pixelgen: Pixel diffusion beats latent diffusion with perceptual loss (2026),https://arxiv.org/abs/2602.02493

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  27. [27]

    Proceedings of the Royal Society of London

    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London. B. Biological Sciences207(1167), 187–217 (02 1980).https://doi. org/10.1098/rspb.1980.0020,https://doi.org/10.1098/rspb.1980.0020

    work page doi:10.1098/rspb.1980.0020 1980

  28. [28]

    org/abs/2501.05427

    Meng, X., Wang, C., Lei, J., Daniilidis, K., Gu, J., Liu, L.: Zero-1-to-g: Taming pretrained 2d diffusion model for direct 3d generation (2025),https://arxiv. org/abs/2501.05427

    work page arXiv 2025

  29. [29]

    Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: Point-e: A system for generating 3d point clouds from complex prompts (2022),https://arxiv.org/ abs/2212.08751

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  30. [30]

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W.,Howes,R.,Huang,P.Y.,Li,S.W.,Misra,I.,Rabbat,M.,Sharma,V.,Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without su...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  31. [31]

    In: Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track (2021)

    Park, D.H., Azadi, S., Liu, X., Darrell, T., Rohrbach, A.: Benchmark for com- positional text-to-image synthesis. In: Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track (2021)

    2021

  32. [32]

    Peebles, W., Xie, S.: Scalable diffusion models with transformers (2023),https: //arxiv.org/abs/2212.09748

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  33. [33]

    Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion (2022),https://arxiv.org/abs/2209.14988

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  34. [34]

    Duy and P

    Qiu, L., Chen, G., Gu, X., Zuo, Q., Xu, M., Wu, Y., Yuan, W., Dong, Z., Bo, L., Han, X.: Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d (2023),https://arxiv.org/abs/2311.16918 26 C. Duy and P. Nguyen

    work page arXiv 2023

  35. [35]

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021),https://arxiv.org/abs/ 2103.00020

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  36. [36]

    Shi,Y.,Wang,P.,Ye,J.,Long,M.,Li,K.,Yang,X.:Mvdream:Multi-viewdiffusion for 3d generation (2024),https://arxiv.org/abs/2308.16512

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  37. [37]

    Sitzmann, V., Rezchikov, S., Freeman, W.T., Tenenbaum, J.B., Durand, F.: Light field networks: Neural scene representations with single-evaluation rendering (2022),https://arxiv.org/abs/2106.02634

    work page arXiv 2022

  38. [38]

    Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B., Liu, Y.: Roformer: Enhanced transformer with rotary position embedding (2023),https://arxiv.org/abs/ 2104.09864

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  39. [39]

    Szymanowicz, S., Rupprecht, C., Vedaldi, A.: Splatter image: Ultra-fast single-view 3d reconstruction (2024),https://arxiv.org/abs/2312.13150

    work page arXiv 2024

  40. [40]

    org/abs/2402.05054

    Tang, J., Chen, Z., Chen, X., Wang, T., Zeng, G., Liu, Z.: Lgm: Large multi-view gaussian model for high-resolution 3d content creation (2024),https://arxiv. org/abs/2402.05054

    work page arXiv 2024

  41. [41]

    Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: Dreamgaussian: Generative gaussian splatting for efficient 3d content creation (2024),https://arxiv.org/abs/2309. 16653

    2024

  42. [42]

    Tang, S., Zhang, F., Chen, J., Wang, P., Furukawa, Y.: Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion (2023), https://arxiv.org/abs/2307.01097

    work page arXiv 2023

  43. [43]

    Team, T.H.: Hunyuan3d 2.5: Towards high-fidelity 3d assets generation with ulti- mate details (2025),https://arxiv.org/abs/2506.16504

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  44. [44]

    Wang, S., Gao, Z., Zhu, C., Huang, W., Wang, L.: Pixnerd: Pixel neural field diffusion (2025),https://arxiv.org/abs/2507.23268

    work page arXiv 2025

  45. [45]

    Wang, Z., Lu, C., Wang, Y., Bao, F., Li, C., Su, H., Zhu, J.: Prolificdreamer: High- fidelity and diverse text-to-3d generation with variational score distillation (2023), https://arxiv.org/abs/2305.16213

    work page arXiv 2023

  46. [46]

    Wang, Z., Wang, Y., Chen, Y., Xiang, C., Chen, S., Yu, D., Li, C., Su, H., Zhu, J.: Crm: Single image to 3d textured mesh with convolutional reconstruction model (2024),https://arxiv.org/abs/2403.05034

    work page arXiv 2024

  47. [47]

    Xiang, J., Chen, X., Xu, S., Wang, R., Lv, Z., Deng, Y., Zhu, H., Dong, Y., Zhao, H., Yuan, N.J., Yang, J.: Native and compact structured latents for 3d generation (2025),https://arxiv.org/abs/2512.14692

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  48. [48]

    Xiang, J., Lv, Z., Xu, S., Deng, Y., Wang, R., Zhang, B., Chen, D., Tong, X., Yang, J.: Structured 3d latents for scalable and versatile 3d generation (2025), https://arxiv.org/abs/2412.01506

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  49. [49]

    Xu, J., Cheng, W., Gao, Y., Wang, X., Gao, S., Shan, Y.: Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models (2024),https://arxiv.org/abs/2404.07191

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  50. [50]

    Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagere- ward: Learning and evaluating human preferences for text-to-image generation (2023),https://arxiv.org/abs/2304.05977

    work page arXiv 2023

  51. [51]

    Xu, Y., Shi, Z., Yifan, W., Chen, H., Yang, C., Peng, S., Shen, Y., Wetzstein, G.: Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation (2024),https://arxiv.org/abs/2403.14621

    work page arXiv 2024

  52. [52]

    Ye, H., Zhang, J., Liu, S., Han, X., Yang, W.: Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models (2023),https://arxiv.org/ abs/2308.06721 PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation 27

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  53. [53]

    Yi, T., Fang, J., Wang, J., Wu, G., Xie, L., Zhang, X., Liu, W., Tian, Q., Wang, X.: Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models (2024),https://arxiv.org/abs/2310.08529

    work page arXiv 2024

  54. [54]

    Yu, M., Lu, T., Xu, L., Jiang, L., Xiangli, Y., Dai, B.: Gsdf: 3dgs meets sdf for improved rendering and reconstruction (2024),https://arxiv.org/abs/2403. 16964

    2024

  55. [55]

    Yu, Y., Xiong, W., Nie, W., Sheng, Y., Liu, S., Luo, J.: Pixeldit: Pixel diffusion transformers for image generation (2026),https://arxiv.org/abs/2511.20645

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  56. [56]

    Zhang, B., Fang, C., Shrestha, R., Liang, Y., Long, X., Tan, P.: Rade-gs: Raster- izing depth in gaussian splatting (2024),https://arxiv.org/abs/2406.01467

    work page arXiv 2024

  57. [57]

    Zhang, B., Cheng, Y., Yang, J., Wang, C., Zhao, F., Tang, Y., Chen, D., Guo, B.: Gaussiancube: A structured and explicit radiance representation for 3d generative modeling (2024),https://arxiv.org/abs/2403.19655

    work page arXiv 2024

  58. [58]

    Zhang, L., Wang, Z., Zhang, Q., Qiu, Q., Pang, A., Jiang, H., Yang, W., Xu, L., Yu, J.: Clay: A controllable large-scale generative model for creating high-quality 3d assets (2024),https://arxiv.org/abs/2406.13897

    work page arXiv 2024

  59. [59]

    Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric (2018),https://arxiv.org/ abs/1801.03924

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  60. [60]

    Zhao, Z., Liu, W., Chen, X., Zeng, X., Wang, R., Cheng, P., Fu, B., Chen, T., Yu, G., Gao, S.: Michelangelo: Conditional 3d shape generation based on shape-image- text aligned latent representation (2023),https://arxiv.org/abs/2306.17115

    work page arXiv 2023