DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation

Bangbang Yang; Chenguo Lin; Panwang Pan; Yadong Mu; Zeming Li

REVIEW 4 major objections 6 minor 3 cited by

DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A fine-tuned text-to-image diffusion model can directly generate multi-view 3D Gaussian splat grids, with a rendering loss enforcing consistency across arbitrary views.

desk verdict DiffSplat is a genuinely useful and carefully ablated method for direct 3DGS generation via fine-tuned image diffusion, with a real but acknowledged pseudo-GT bottleneck and a slightly overstated 3D-consistency claim. read the letter →

arxiv 2501.16764 v1 pith:KU6MWGZK submitted 2025-01-28 cs.CV

Chenguo Lin , Panwang Pan , Bangbang Yang , Zeming Li , Yadong Mu This is my paper · ORCID

classification cs.CV

keywords text-to-3Dgenerationimage-to-3D3DGaussiansplattinglatentdiffusionmodelsmulti-viewconsistencyrenderinglosssplatlatentscontrollable

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

DiffSplat argues that the most practical route to scalable text- and image-to-3D generation is to repurpose large pretrained text-to-image diffusion models rather than train 3D networks from scratch. The paper shows that 3D Gaussian splat properties can be laid out as multi-view 2D grids, compressed by a fine-tuned image VAE into 'splat latents', and denoised by an image diffusion model fine-tuned with two losses: an ordinary diffusion loss plus a differentiable rendering loss that checks the generated splats against ground-truth images from arbitrary viewpoints. If correct, this gives a single unified model that inherits web-scale 2D priors, produces view-consistent 3D, and can adopt image-generation tools such as ControlNet without architectural changes. The paper reports that this approach outperforms previous native-3D and reconstruction-based generators on prompt alignment and image fidelity, while training in about three days on eight A100 GPUs.

What carries the argument

The central object is the splat latent: a multi-view 2D grid of 3D Gaussian splat properties (color, position, scale, rotation quaternion, opacity), encoded by a fine-tuned image VAE into the latent space of a pretrained image diffusion model. The load-bearing mechanism is the two-term training objective, where the diffusion loss teaches the denoiser to produce plausible splat latents and the rendering loss decodes the denoised latents into Gaussian primitives, rasterizes them with 3D Gaussian Splatting, and compares the rendered images to ground-truth views, thereby enforcing multi-view consistency directly in 3D. Plücker embeddings are concatenated along the feature dimension to inject camera pose information into the multi-view generation process.

What would settle it

Hold out a set of objects with real multi-view captures, generate splats from text and image prompts, render them from viewpoints never used in training, and compare silhouette, depth, and photo metrics to the ground-truth views. If removing the rendering loss from the training objective does not measurably reduce cross-view consistency, or if replacing the reconstructed pseudo-ground-truth grids with real-scanned splat grids leaves output quality unchanged, the paper's two central load-bearing claims would be refuted.

Watch

Extended reading notes

Core claim

The central claim is that 3D Gaussian splat grids are image-like enough that a pretrained latent diffusion model can be fine-tuned to generate them directly, provided the VAE is adapted to splat properties and the training adds a rendering loss. The paper introduces a lightweight reconstruction model that regresses per-pixel Gaussian primitives from four posed views in under 0.1 seconds, producing pseudo-ground-truth multi-view splat grids for scalable dataset curation. These grids are encoded by a fine-tuned image VAE into splat latents, which the denoising network generates under text or image conditioning. The final objective combines a standard diffusion loss on splat latents with a rendering loss evaluated by decoding denoised latents into Gaussian properties and rendering them from random viewpoints; the rendering loss is what enforces 3D coherence that a purely 2D diffusion loss leaves implicit. In the reported experiments, DiffSplat beats native-3D models and two-stage reconstruction models on prompt alignment and image fidelity metrics, and switching the base model from SD1.5 to SDXL, PixArt-α/Σ, or SD3 improves results monotonically.

Load-bearing premise

The load-bearing premise is that the lightweight reconstruction model's output is good enough to serve as ground truth for diffusion training; if those pseudo-ground-truth splat grids are biased or lose detail, the generated 3D content cannot surpass them.

Editorial extensions

If this is right

A single fine-tuned image diffusion model, not a separate multi-view generator plus reconstruction module, can produce directly renderable 3D Gaussian splats from text or a single image, eliminating the two-stage pipeline collapse failure mode.
Any improvement in base text-to-image models (architecture, noise schedule, sampling) transfers to 3D generation by swapping the backbone, as evidenced by monotonic gains from SD1.5 to SD3.
Image-diffusion techniques such as ControlNet work on 3D generation unchanged, enabling depth-, normal-, and edge-controlled text-to-3D and text-guided single-image reconstruction.
Text- and image-conditioned generation can be unified in one model; the paper reports higher CLIP similarity, CLIP R-Precision, and ImageReward on T3Bench prompts and higher PSNR/SSIM/lower LPIPS on GSO images than compared methods.
Only multi-view images are needed for supervision, not ground-truth 3D meshes or per-instance optimization, so the pipeline can in principle scale to web-scale real-world video and photo datasets.

Reading between the lines

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

If the pseudo-ground-truth ceiling is real, future gains may come less from bigger diffusion backbones than from better reconstruction models or from mixing real multi-view data into the splat-latent training set.
The same 'encode a 3D representation into image-latent space and add a rendering loss' recipe might extend to other differentiable 3D representations such as meshes, signed distance fields, or neural radiance fields, since only the VAE and rendering layer need to change.
Because the rendering loss supervises arbitrary views, the method could in principle train directly on monocular video without posed multi-view captures, using the video frames themselves as the arbitrary-view supervision; the paper notes this direction but does not demonstrate it.
The controllability results hint that a diffusion prior plus control inputs could substitute for large 3D datasets in niche domains, lowering the cost of producing game and VR assets.

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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

Here's what I'd want you to know about DiffSplat. It's a new combination rather than a tweak: they fine-tune a pretrained text-to-image latent diffusion model to denoise structured 2D grids of Gaussian splat properties, using a lightweight 42M reconstruction model to produce pseudo-ground-truth grids and a VAE fine-tuned with a rendering loss. The extra rendering loss on the denoised output, comparing against real multi-view images, is what gives the training independent grounding beyond the pseudo-labels. That core contribution holds up.

The paper is honestly written and carefully ablated. The ablations show the value of coordinate/normal guidance for the reconstruction model, the importance of fine-tuning the VAE rather than freezing it, the rendering loss's role in reducing translucent floaters, and consistent gains when swapping the backbone from SD1.5 to SDXL, PixArt, and SD3. The T3Bench and GSO numbers are strong, and the claimed training cost (3 days on 8 A100 GPUs) is a practical advantage over native 3D diffusion training.

The real soft spot is the 3D consistency claim. The evaluation uses rendered-image metrics — PSNR/SSIM/LPIPS averaged over viewpoints — which can be satisfied by view-dependent floaters or billboards. The ablation in Fig. 6 shows that without L_render you get more translucent floaters, so the risk is real. The paper itself admits mesh extraction remains unsolved, and the pseudo-GT bottleneck in Sec. 3.3.2 is explicitly acknowledged as limiting the upper bound. They do not directly measure geometric quality. That's a genuine limitation, but it is not a fatal flaw: the rendering loss provides some real multi-view grounding, and the metrics are standard for the field.

Smaller issues: the main comparison tables lack error bars (the ablation table has them), and the closest concurrent 3DGS diffusion baselines like GaussianCube and GSLD are not compared. Code and models are claimed to be public, so the omissions are addressable. One thing the stress-test note got wrong: omega_r(t) is not unspecified — Appendix A states all weighting terms are set to 1.

Who is this for? Anyone working on 3D generation from text or images, particularly methods built on top of 2D diffusion. It deserves a serious referee. My own take is that it's a solid paper with an overstated but defensible central claim, and a useful contribution to the 3DGS generation literature. I'd bring it to the reading group and would cite it.

Referee Report

4 major / 6 minor

Summary. DiffSplat proposes a unified framework for native 3D generation by fine-tuning pretrained text-to-image latent diffusion models to denoise splat latents: VAE-encoded multi-view Gaussian splat grids. A 42M reconstruction model F_theta is trained on G-Objaverse to turn posed RGB images (plus coordinate/normal maps) into structured Gaussian splat grids in under 0.1 s; a pretrained image VAE is fine-tuned to compress these grids into splat latents; and the diffusion backbone is trained with a diffusion loss plus a rendering loss that decodes denoised splat latents and renders them against real multi-view images. Experiments report state-of-the-art text-to-3D metrics on T3Bench and image-to-3D metrics on GSO, with ablations over reconstruction inputs, auto-encoding strategies, multi-view manners, training objectives, and base diffusion models, plus a ControlNet application.

Significance. If correct, the paper provides a practical recipe for transferring web-scale 2D diffusion priors to 3D generation, with a structured Gaussian representation that keeps modifications to the image-diffusion architecture minimal. It ships public code and models, reports low training cost, and includes an unusually thorough set of ablations (Tables 3-5) that isolate the contribution of geometric guidance, VAE fine-tuning, multi-view concatenation, and the rendering loss. The central risk is that the '3D consistency' claim is currently supported only by rendered-image metrics and by qualitative inspection, and the main comparative tables lack error bars. These gaps are fixable but are load-bearing for the paper's strongest claims.

major comments (4)

[§4.2, §4.3, Tables 1 and 2] The headline comparisons against GVGEN, LN3Diff, DIRECT-3D, 3DTopia, LGM, GRM, 3DTopia-XL, and related baselines are reported without error bars or significance tests, even though the ablation study in Table 5 already uses mean +/- std over repetitions. Because text-to-3D and image-to-3D sampling are stochastic and the margins over the strongest baselines are sometimes small (for example, Table 1 reports CLIP Sim. of 30.95 for DiffSplat versus 29.96 for LGM on single-object prompts), the claimed superiority should be backed by at least three random seeds with standard deviations, or by pairwise significance tests. Without this, the central empirical claim is not yet established.
[§4.1, §4.3, and Fig. 6] The paper claims to maintain 3D consistency in a unified model, but the only quantitative evidence is PSNR/SSIM/LPIPS averaged over an unspecified set of rendered viewpoints. These metrics can be satisfied by per-view billboards or translucent floaters; indeed the ablation in Fig. 6 shows that removing L_render produces visible translucent floaters, and the Limitations section states that mesh extraction is unsolved. The evaluation should state whether the rendered views coincide with the generated views or are held-out novel views, and should add a geometry-level test such as depth/normal consistency across views, multi-view silhouette agreement, or Chamfer distance after point-cloud/mesh extraction. This is the key differentiator from two-stage reconstruction-based methods and needs direct evidence.
[§3.3.2, Eq. (6), and Appendix A] The training objective in Eq. (6) contains an unspecified weighting term omega_r(t) for the rendering loss. Appendix A states that all weighting terms are set to 1 and mentions a probability schedule for the rendering loss (starts at 0 and is set to 1 later), but this does not define omega_r(t) as a function of the noise level or specify the activation schedule precisely. Since the rendering loss is evaluated on denoised outputs at randomly sampled t, its behavior at high noise levels is qualitatively different from its behavior near t = 0; without a precise definition the central training objective is not fully reproducible. I recommend specifying omega_r(t) explicitly and reporting an ablation or sensitivity analysis of this schedule.
[§3.1, §3.3.2, and §4.3] The diffusion target z in Eq. (5) is the splat latent produced by the lightweight reconstruction model F_theta, so systematic errors of F_theta (smoothing, missing geometry, coordinate-map artifacts) are inherited by the generative model. The paper acknowledges this upper-bound limitation, and the rendering loss in Eq. (6) partially grounds training in real images, but the residual bottleneck is never quantified. A direct experiment is needed: compare rendered outputs of generated splats against F_theta's own reconstructions for the same prompts or images, and against ground-truth images at held-out views, to measure how much of the final reconstruction error comes from the pseudo-ground-truth pathway. This would also clarify the interpretation of the GSO numbers in Table 2.

minor comments (6)

[§4.1, §4.2, §4.3] There is a missing space in 'conditions. CLIP similarity score' in §4.1, and the section headings 'Results and Comparisions' in §4.2 and §4.3 contain a typo.
[§3.1 and Appendix A] The notation Vin and V is easy to confuse: Eq. (1) sums over V views, while Appendix A sets Vin = 4 and V = 8. Please define both quantities clearly in the main text and use distinct symbols throughout.
[Eq. (4)] L_render is originally defined for a set of Gaussian primitives G, but in Eq. (4) it is applied to the auto-encoded grids; the notation should make explicit that the argument denotes the decoded Gaussian-primitive set obtained from the auto-encoded grids.
[§4.5.2] 'Aesthetic appeal and textured details may contributed by the perceptual loss' should be reworded, for example 'may be contributed by the perceptual loss'.
[§1 and §4.4] The contribution claim that 'numerous techniques' for image generation can be seamlessly adapted to 3D is supported in the experiments only by ControlNet; consider tightening the claim or adding at least one more adaptation, such as personalization or few-step distillation.
[Fig. 7 and §4.5.3] The interpretation that image diffusion models learn a 'special style' when generating splat latents is qualitative; if this is intended as evidence for the method's mechanism, a quantitative analysis (for example, latent-space statistics or reconstruction error as a function of VAE fine-tuning) would strengthen it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the diffusion target is pseudo-labeled, but the rendering loss in Eq. 6 and the VAE objective in Eq. 4 are supervised directly by real multi-view images, so the central claim does not reduce to its inputs.

full rationale

DiffSplat's derivation is self-contained and does not reduce to its inputs. The only quantity derived from the training data before generative training is the pseudo-ground-truth splat grid G = F_theta(multi-view images), which is used as the regression target for the diffusion loss in Eq. 5. This is an explicitly acknowledged pseudo-label design: the paper states that treating splat latents as ground-truth samples 'limits the upper bound of the generative model, as real multi-view datasets are not involved in the training process' (Sec. 3.3.2). Crucially, this pseudo-label loop is not the only supervision. Eq. 6 adds a rendering loss on D_phi(F_psi(z_t,t)) supervised directly by ground-truth multi-view images, and Eq. 4 fine-tunes the VAE with L_render on real renders. Both provide independent image-space grounding that can correct errors inherited from F_theta, so the model's output is not forced by construction to equal its pseudo-GT input. Evaluation is performed on held-out GSO objects and T3Bench prompts, and the reported superiority over baselines is an empirical claim supported by Tables 1-2 rather than a definitional identity. The only self-citation (Pan et al., 2024, in Related Work) is a contextual example of reconstruction-based methods and carries no load in the derivation. Concerns about view-dependent floaters or mesh extraction are correctness and evaluation limitations, not circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central contribution is empirical. The method relies on the transferability of 2D diffusion priors to a new latent domain, on the quality of the reconstruction-model pseudo-labels, and on the effectiveness of differentiable rendering as a training signal. No new physical entities are postulated. The free parameters are training hyperparameters rather than fitted constants in a derivation.

free parameters (3)

Gaussian scale interpolation bounds smin, smax = 5e-4, 2e-2
Hand-set in Appendix A; Eq. 2 maps raw network output to [smin, smax]. These bounds control primitive sizes and are not learned or swept.
Loss weighting terms lambda_p, lambda_alpha, lambda_r, lambda_diff, lambda_render = 1 (all set to 1)
In Eqs. 1, 4, and 6, these hyperparameters are stated to be set to 1 (Appendix A reports 'All weighting terms are set to 1'). No sensitivity analysis is reported.
Rendering-loss noise weighting omega_r(t) = unspecified
Appears in Eq. 6 as a function of noise level t; the paper does not provide its definition or schedule, which is a reproducibility gap. It controls how strongly the rendering loss acts at different diffusion timesteps.

assumptions (5)

domain assumption Pretrained text-to-image diffusion models encode geometric priors that transfer to Gaussian splat grid latents after fine-tuning.
Central hypothesis motivating the method; supported by geometry estimation works cited in Sec. 3 but not proven for splat grids.
domain assumption A lightweight Transformer reconstruction model conditioned on multi-view RGB, coordinate, and normal maps can produce high-quality Gaussian splat grids in under 0.1 seconds.
Sec. 3.1 and Appendix A; this is the pseudo-label generator whose quality bounds the generative model.
domain assumption Fine-tuning the pretrained VAE with reconstruction and rendering losses yields a latent space (splat latents) close enough to natural image latents that the pretrained diffusion decoder remains effective.
Sec. 3.2; empirically validated in Table 4 but not guaranteed by architecture.
domain assumption Differentiable rendering through the VAE decoder provides useful gradients for the diffusion model at all sampled noise levels t.
Sec. 3.3.2 Eq. 6; relies on omega_r(t) to balance noisy denoised latents, and omega_r(t) is not specified.
domain assumption G-Objaverse (265K objects) with Cap3D captions is a sufficient and representative training distribution.
Sec. 4.1; limits generality to object-centric scenes at 256x256 resolution.

invented entities (1)

Splat latent space (multi-view Gaussian splat grids encoded by a fine-tuned image VAE)
purpose: Enables a pretrained image diffusion model to generate 3D Gaussian splats in a latent domain that resembles natural image latents.
The representation is introduced and validated only through this paper's experiments; there is no external falsifiable handle beyond rendered image quality measurements on the paper's chosen benchmarks.

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Cite this review

Pith. "Pith review of DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation." pith.science (2026). https://pith.science/paper/KU6MWGZK

@misc{pith2026250116764,
  author       = {Pith},
  title        = {Pith review of: DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KU6MWGZK}},
  note         = {Machine review of arXiv:2501.16764}
}

read the original abstract

Recent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation. We introduce DiffSplat, a novel 3D generative framework that natively generates 3D Gaussian splats by taming large-scale text-to-image diffusion models. It differs from previous 3D generative models by effectively utilizing web-scale 2D priors while maintaining 3D consistency in a unified model. To bootstrap the training, a lightweight reconstruction model is proposed to instantly produce multi-view Gaussian splat grids for scalable dataset curation. In conjunction with the regular diffusion loss on these grids, a 3D rendering loss is introduced to facilitate 3D coherence across arbitrary views. The compatibility with image diffusion models enables seamless adaptions of numerous techniques for image generation to the 3D realm. Extensive experiments reveal the superiority of DiffSplat in text- and image-conditioned generation tasks and downstream applications. Thorough ablation studies validate the efficacy of each critical design choice and provide insights into the underlying mechanism.

Figures

Figures reproduced from arXiv: 2501.16764 by the authors.

**Figure 1.** Comparison with Previous 3D Diffusion Generative Models. (1) Native 3D methods and (2) rendering-based methods encounter challenges in training 3D diffusion models from scratch with limited 3D data. (3) Reconstruction-based methods struggle with inconsistencies in generated multi-view images. In contrast, (4) DIFFSPLAT leverages pretrained image diffusion models for the direct 3DGS generation, effectively utilizing … view at source ↗

**Figure 2.** Method Overview. (1) A lightweight reconstruction model provides high-quality structured representation for “pseudo” dataset curation. (2) Image VAE is fine-tuned to encode Gaussian splat properties into a shared latent space. (3) DIFFSPLAT is natively capable of generating 3D contents by image and text conditions utilizing 2D priors from text-to-image diffusion models. 2023) across random V viewpoints, and N = Vin… view at source ↗

**Figure 3.** Qualitative Results and Comparisons on Text-conditioned 3D Generation. More visualizations of DIFFSPLAT results are provided in Appendix [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (11 more)

**Figure 4.** Figure 4: Qualitative Results and Comparisons on Image-conditioned 3D Generation. More visualizations of DIFFSPLAT results are provided in Appendix [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: Controllable Generation. ControlNet can seamlessly adapt to DIFFSPLAT for controllable text-to-3D generation in various formats, such as normal and depth maps, and Canny edges [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 7.** Figure 7: Splat Latents Visualization. 3DGS properties are structured in grids. “Decoded GS” shows the splat latents decoded by an image diffusion VAE. To understand the feasibility of generating Gaussian splat properties through fine-tuning image diffusion models, we visualize …

**Figure 6.** Figure 6: Ablation of Lrender. Both text- (1st row) and image-conditioned (2nd row) DIFFSPLAT with Lrender produces more aesthetic and textured 3D content with fewer translucent floaters. 5 CONCLUSION In this work, we present a novel diffusion-based 3D generation framework, DIFF…

**Figure 8.** Figure 8: Controllable Generation with Multi-modal Conditions. DIFFSPLAT can effectively utilize both text and image conditions for single-view reconstruction with text understanding. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

**Figure 9.** Figure 9: More results of text-conditioned DIFFSPLAT. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

**Figure 10.** Figure 10: More results of text-conditioned DIFFSPLAT. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

**Figure 11.** Figure 11: More results of text-conditioned DIFFSPLAT. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_11.png]

**Figure 12.** Figure 12: More results of image-conditioned DIFFSPLAT. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]

**Figure 13.** Figure 13: More results of image-conditioned DIFFSPLAT. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]

**Figure 14.** Figure 14: More results of image-conditioned DIFFSPLAT. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_14.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization
cs.CV 2026-08 conditional novelty 6.0 of 10

A training-free graph-cut method selects 3D objects from Gaussian splatting scenes using sparse user scribbles, reaching 92.2 mIoU on NVOS with three interaction views.
GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion
cs.CV 2026-07 conditional novelty 6.0 of 10

A dual-stream feed-forward 3DGS codec plus geometry-guided one-step diffusion decoding beats cascaded compressors on rate-distortion while keeping multi-view consistency.
PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation
cs.CV 2026-07 unverdicted novelty 6.0 of 10

PixGS is a single-stage pixel-space diffusion model that directly produces high-quality 3D Gaussian Splats from text or images in ~1s, outperforming multi-stage latent methods on standard benchmarks.

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Runtime for D IFFSPLAT to generate a single 3D object on an A100 GPU is only about 1∼2 seconds with half precision. Cost Notably, with 2D generative priors, DIFFSPLAT only takes about 3 days on 8 A100 GPUs to generate decent results with fp16 mixed precision, which is much mor...

2025
[2016]

with linear warm-up are adopted for parameter optimization. Inference For diffusion-based models (SD1.5 (Rombach et al., 2022), SDXL (Podell et al., 2024), PixArt-α (Chen et al., 2024c) and PixArt- Σ (Chen et al., 2024b)), the DPM-Solver++ (Lu et al., 2022a;b) ODE solver with ...

2022
[2017]

smin and smax are set to 5e-4 and 2e-2 respectively to repre- sent fine-grain details

with 512 attention dimensions and a patch size of 8, whose parameter size is only 42M and 9.9%∼23% of previous methods (Tang et al., 2024; Xu et al., 2024c; Zhang et al., 2024c). smin and smax are set to 5e-4 and 2e-2 respectively to repre- sent fine-grain details. The input v...

2024
[2018]

Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems (NeurIPS), 2022a. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen,...

2025 arXiv
[2020]

3dtopia: Large text-to-3d generation model with hybrid diffusion priors

Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Tengfei Wang, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.02234, 2024a. Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai ...

arXiv
[2021]

Flexible isosurface extraction for gradient- based mesh optimization

14 Published as a conference paper at ICLR 2025 Tianchang Shen, Jacob Munkberg, Jon Hasselgren, Kangxue Yin, Zian Wang, Wenzheng Chen, Zan Gojcic, Sanja Fidler, Nicholas Sharp, and Jun Gao. Flexible isosurface extraction for gradient- based mesh optimization. ACM Transactions ...

2025
[2022]

Repurposing diffusion-based image generators for monocular depth estimation

12 Published as a conference paper at ICLR 2025 Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer...

2025
[2023]

Sampling 3d gaussian scenes in seconds with latent diffusion models

Paul Henderson, Melonie de Almeida, Daniela Ivanova, and Titas Anciukevi ˇcius. Sampling 3d gaussian scenes in seconds with latent diffusion models. arXiv preprint arXiv:2406.13099,

arXiv
[2024]

Sf3d: Stable fast 3d mesh reconstruction with uv-unwrapping and illumination disentanglement

Mark Boss, Zixuan Huang, Aaryaman Vasishta, and Varun Jampani. Sf3d: Stable fast 3d mesh reconstruction with uv-unwrapping and illumination disentanglement. arXiv preprint arXiv:2408.00653,

arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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Lara: Efficient large- baseline radiance fields

Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large- baseline radiance fields. In European Conference on Computer Vision (ECCV), 2024a. Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- σ: Weak-to-strong training of diffusion transfor...

arXiv

[2] [3]

Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image

11 Published as a conference paper at ICLR 2025 Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision (ECCV),

work page 2025

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Lumina-t2x: Transforming text into any modality, resolution, and duration via flow-based large diffusion transformers

Peng Gao, Le Zhuo, Ziyi Lin, Chris Liu, Junsong Chen, Ruoyi Du, Enze Xie, Xu Luo, Longtian Qiu, Yuhang Zhang, et al. Lumina-t2x: Transforming text into any modality, resolution, and duration via flow-based large diffusion transformers. arXiv preprint arXiv:2405.05945, 2024a. Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-B...

arXiv

[4] [5]

T3bench: Benchmarking current progress in text-to-3d generation

Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T3bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977,

arXiv

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Classifier-free diffusion guidance

Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications ,

work page 2021

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Instant3d: Fast text-to-3d with sparse-view gen- eration 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 gen- eration and large reconstruction model. In International Conference on Learning Representations (ICLR), 2024a. Weiyu Li, Rui Chen, Xuelin Chen, and Ping Tan. Sweetdreamer: Align...

arXiv

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View selection for 3d captioning via diffusion rank- ing

Tiange Luo, Justin Johnson, and Honglak Lee. View selection for 3d captioning via diffusion rank- ing. arXiv preprint arXiv:2404.07984,

work page arXiv

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Point-e: A system for generating 3d point clouds from complex prompts

Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751,

arXiv