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FRIDU: Functional Map Refinement with Guided Image Diffusion

T0 review · 2 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read FRIDU treats a functional map matrix as an image and trains a guided diffusion model to refine noisy shape correspondences, with the refined maps competitive with state-of-the-art methods on FAUST, SCAPE, and SHREC19.

desk verdict Novel diffusion-on-functional-maps idea with solid in-distribution refinement, but the SOTA-competitive claim is undermined by test-set tuning and a clear SHREC19 miss. read the letter →

arxiv 2506.14322 v1 pith:YRA3MJC3 submitted 2025-06-17 cs.CV cs.LG

Avigail Cohen Rimon , Mirela Ben-Chen , Or Litany This is my paper · ORCID
classification cs.CVcs.LG
keywords functionalmapsshapecorrespondencediffusionmodelsimagemaprefinementpoint-to-pointguidancespectralgeometrygeodesicerror
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 sets out to prove that functional map refinement can be solved by image diffusion: a functional map matrix, normalized to pixel values, is a valid 2D image, and a diffusion model trained to denoise such images conditioned on a noisy initial map produces accurate refined maps. Training is done entirely in spectral space, so no point-to-point maps are needed during learning. At inference, the pointwise map recovered from the current functional map is injected as guidance, optionally alongside orthogonality or Laplacian-commutativity objectives. The authors report that the method is competitive with dedicated refinement algorithms, improving FAUST mean geodesic error from 2.5 initially to 1.5 with orthogonality guidance. If correct, it makes diffusion models a flexible, plug-and-play tool for functional-map processing that adapts to new tasks through guidance alone.

What carries the argument

The central object is the functional map matrix itself, treated as an image patch. The machinery is a patch-based EDM-DDPM++ diffusion model that denoises corrupted functional maps, conditioned on the initial map and on extra position channels encoding where each patch lies in the full matrix. At inference, guidance from the point-to-point map—solved by nearest-neighbor search under the smoothness prior and held fixed during backpropagation—injects the geometric requirement that the functional map come from a valid dense map; orthogonality and Laplacian-commutativity losses add optional spectral regularizers.

What would settle it

Take a trained model and feed it initial maps whose quality is systematically degraded (e.g., by increasing descriptor noise, reducing the spectral basis, or using a distant shape class), then plot refined-map geodesic error against initial-map error. If there is a corruption level beyond which refinement stops improving the map or makes it worse, the distributional assumption is violated. A cleaner test: train only on WKS-based maps and apply the model to SHOT-based initial maps on a held-out shape class, and check whether refinement still consistently lowers error.

Watch

Extended reading notes

Core claim

The central claim is that a functional map—a change-of-basis matrix between two shapes' spectral embeddings—can be refined by treating it as an image and running a conditioned image diffusion model. The model learns, from pairs of noisy initial maps and ground-truth maps, to denoise a corrupted functional map while conditioning on the initial map and on the spatial position of each patch. At test time, the point-to-point map corresponding to the current diffusion sample is computed by nearest-neighbor search and used as a guidance objective, with gradients stopped through that extraction; the paper argues this indirect signal steers generation toward maps consistent with valid dense correspondences. The result is a refinement pipeline that works for initial maps from different descriptors and learned features, generalizes across shape classes, and matches or beats existing refinement methods on FAUST, SCAPE, and SHREC19.

Load-bearing premise

The noisy initial maps seen at test time must lie close enough to the distribution of descriptor-based maps used during training that the learned diffusion prior can correct them; the paper states the method is 'somewhat sensitive to the initial map'.

Editorial extensions

If this is right

  • The same trained model can refine functional maps from different descriptor sources (WKS, SHOT) and from learned feature extractors, including zero-shot across descriptor types.
  • Because training is purely in spectral space and uses patches, refinement can be learned from very small datasets, such as 190 correspondence pairs.
  • Inference-time guidance for orthogonality or Laplacian commutativity changes the output without retraining, letting one model serve multiple tasks.
  • A model trained on one shape class (Michael) improves maps on unseen human shapes (FAUST) and a non-human animal (wolf), indicating cross-category generalization.
  • One recursive refinement iteration improves accuracy further, while additional iterations degrade it.

Reading between the lines

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

  • One consequence the authors leave implicit: the same image-diffusion treatment could extend to other spectral operators, such as shape difference operators or functional vector fields, since those are also matrices on the spectral basis; the paper lists this as future work.
  • If a diffusion prior were trained on a broad distribution of noisy correspondences across many shape classes, guidance alone might enable zero-shot refinement for new datasets and pipelines, avoiding per-dataset training—this is the paper's stated foundation-model vision.
  • The patch-based, position-encoded training suggests the model's effective receptive field is limited; a testable extension is whether training with larger patches or full maps on larger datasets removes the remaining sensitivity to very bad initializations.
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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

2 major / 5 minor

Summary. The paper proposes FRIDU, a method that treats functional map matrices as 2D images and trains a conditional image diffusion model to denoise/refine an initial, inaccurate functional map. At inference, a point-to-point (P2P) guidance term, optionally combined with orthogonality or Laplacian-commutativity regularizers, steers the diffusion process. Training is performed purely on functional maps (only the preprocessing uses ground-truth P2P maps), and a patch-based diffusion strategy is used for efficiency. Experiments cover descriptor-based initial maps (WKS, SHOT) on the Michael/TACO dataset, and feature-based initial maps from DiffZO on FAUST, SCAPE, and SHREC19. The paper reports improved accuracy over the initial maps in most settings and competitive in-distribution results with DiffZO, but substantially worse cross-dataset results on SHREC19.

Significance. Treating a functional map matrix as an image and applying a conditional diffusion model is a novel and nontrivial reformulation; the patch-based training on small datasets is a practical contribution, and the plug-and-play guidance framework is flexible. The in-distribution evidence is credible: on FAUST, FRIDU with orthogonality guidance reaches 1.5 mean geodesic error versus 1.9 for DiffZO, and the Michael/WKS and SHOT experiments show consistent refinement over initial maps. The paper is also honest about its limitations. However, the only held-out benchmark in the main comparison, SHREC19, shows FRIDU at 9.4/7.1 versus DiffZO at 4.2/3.6, and the reported cross-dataset numbers use a guidance strength selected on that test set. The cross-dataset generalization claim is thus not established, and the strength of the paper rests on the in-distribution refinement results.

major comments (2)
  1. [Section 3.3] The SHREC19 rows of Table 1 are obtained with guidance strength s=2000, not the default s=500 used elsewhere. The text states: 'For cross-dataset evaluations in Section 3.2, we find that increasing s to 2000 improves performance.' Because SHREC19 is the only held-out dataset in Table 1, selecting s after observing its test errors is a form of test-set tuning. Please report the SHREC19 results with the default s=500 (and with orthogonality guidance, if applicable), or provide a validation-based selection protocol that does not use the SHREC19 test set. This is load-bearing because the cross-dataset numbers are already the weakest part of the comparison.
  2. [Section 4 / Table 1] The paper's own limitation statement ('somewhat sensitive to the initial map... for a very bad initialization... does not fully reach the optimal solution') applies precisely to the SHREC19 setting, where the initial maps are very noisy (initial error 12.4 for Train F) and come from a different feature extractor than the training distribution. With those initial maps, FRIDU reduces the error to 9.4 whereas DiffZO, starting from the same initial condition, reaches 4.2. The central claim that the learned prior refines maps between 'arbitrary shape pairs' is therefore only supported in the in-distribution regime; the cross-dataset regime, which is the most valuable case for a learned refinement prior, is not established. I would like the authors to discuss this distribution-shift limitation explicitly and temper the 'comparable' wording in Section 3.2 accordingly.
minor comments (5)
  1. [Appendix A and B] The appendix references 'Section 4.2' and 'Section 4.1' (e.g., 'except in Section 4.2, where we match the resolution used in DiffZO' and 'for the experiments in Section 4.1'), but the paper's sections are numbered 1-5 and the experiments are in Section 3. Please correct these cross-references.
  2. [Throughout] The baseline name is spelled inconsistently as both 'DiffZO' (Section 3.2, Table 1) and 'DiffZo' (Figure 9 caption, Appendix C). Please standardize.
  3. [Section 3.2] The sentence 'Our method consistently improves the initial mapping and outperforms DiffZO in intra-dataset evaluations, while maintaining comparable results in cross-dataset evaluations relative to supervised methods' is ambiguous. Since the primary comparison is DiffZO, the SHREC19 gap (9.4 vs 4.2) should be stated explicitly rather than qualified as 'comparable.'
  4. [Section 2.6, Eq. (7)] The stop-gradient operation for Π21 is described in prose but not shown in the equation. Please indicate the stop-gradient (e.g., using a ⊥ symbol or an explicit 'detach' notation) to make the guidance loss self-contained.
  5. [Section 3.3] There is a typo: 'the number of recurrent stepsk' should read 'the number of recurrent steps k.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FRIDU's refinement is trained on independently constructed ground-truth functional maps and uses standard diffusion and guidance objectives, with no fitted parameter or self-citation chain forcing the reported predictions.

full rationale

The paper's derivation chain is self-contained and empirically grounded rather than circular. The training objective (Eqs. 2 and 4) fits a diffusion model d_theta to recover the ground-truth functional map C*_ij from a noisy version, conditioned on an initial functional map eC_ij. The ground-truth targets are computed independently from ground-truth point-to-point maps via C*_ij = Phi^dagger_i Pi*_ij Phi_j (Section 2.2), and the initial maps are produced by descriptor-based methods, by WKS/SHOT computations, or by DiffZO feature extraction (Section 3.2, Appendix C); none of these inputs is defined in terms of the model's output or the evaluation metric. At inference, Eq. (3) is the standard deterministic EDM update, and the P2P guidance in Eq. (7) is a regularizer derived from the classical functional-map-to-pointwise-map projection of Eq. (5), attributed to Ezuz and Ben-Chen [EBC17]. This is a background methodological citation, not a self-citation invoked to forbid alternatives or to supply an unverified uniqueness theorem. The orthogonality and Laplacian-commutativity guidance terms are also standard regularizers from the literature. Recursive refinement (Section 2.7) feeds the model its own output, but the paper reports this as an experimental variant with observed degradation, not as a derivation that assumes its conclusion. The only notable concerns are experimental-validity issues, not circularity: the SHREC19 results were obtained with guidance strength s=2000 selected after looking at cross-dataset performance, whereas the default is s=500 (Section 3.3), and Section 4 concedes the method is 'somewhat sensitive to the initial map' and does not fully recover from very bad initializations. These are limitations of generalization and hyperparameter selection, but they do not make any prediction equivalent to an input by construction. No load-bearing step reduces to a fitted constant, a renamed input, or a self-referential derivation, so the appropriate finding is no significant circularity.

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

The paper rests on standard spectral geometry assumptions and on the empirical validity of diffusion-based denoising. No new physical entities are introduced. The main burden is the assumption that a diffusion prior trained on descriptor-based maps transfers to unseen initializations.

free parameters (4)
  • guidance strength s = 500 (2000 for cross-dataset)
    Selected to balance error and runtime; increased to 2000 for cross-dataset evaluations after observing performance (Sec 3.3).
  • guidance gradient steps m = 2
    Ablated over 1, 2, 10 on the horse benchmark; chosen as the trade-off point (Table 2).
  • guidance recurrent steps k = 5
    Ablated over 1, 5, 10; chosen as the trade-off point (Table 2).
  • patch size schedule = 3 resolutions
    Patch-Diffusion hyperparameter; the number of patch resolutions affects data efficiency and is not derived from theory (Appendix A).
assumptions (4)
  • domain assumption Functional maps represented in the Laplace-Beltrami eigenbasis provide a valid and sufficient representation for shape correspondence.
    Used throughout the paper; inherited from Ovsjanikov et al. [OBCS*12] and standard in the field.
  • domain assumption A diffusion model trained to denoise functional map matrices in image space learns a useful prior for valid maps.
    Central to the method; supported only empirically by the reported results, not by a theoretical guarantee.
  • domain assumption Ground truth point-to-point maps used to generate training functional maps are correct and available for the training shapes.
    Training loss (Eq. 2) uses C* derived from GT P2P maps; the paper assumes these are reliable.
  • domain assumption P2P guidance computed by solving Eq. (5) with Pi treated as a constant still steers the diffusion process toward valid maps.
    Justified only by the iterative nature of denoising (Sec 2.6), not by a convergence theorem.

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Cite this review

Pith. "Pith review of FRIDU: Functional Map Refinement with Guided Image Diffusion." pith.science (2026). https://pith.science/paper/YRA3MJC3

@misc{pith2026250614322,
  author       = {Pith},
  title        = {Pith review of: FRIDU: Functional Map Refinement with Guided Image Diffusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YRA3MJC3}},
  note         = {Machine review of arXiv:2506.14322}
}
read the original abstract

We propose a novel approach for refining a given correspondence map between two shapes. A correspondence map represented as a functional map, namely a change of basis matrix, can be additionally treated as a 2D image. With this perspective, we train an image diffusion model directly in the space of functional maps, enabling it to generate accurate maps conditioned on an inaccurate initial map. The training is done purely in the functional space, and thus is highly efficient. At inference time, we use the pointwise map corresponding to the current functional map as guidance during the diffusion process. The guidance can additionally encourage different functional map objectives, such as orthogonality and commutativity with the Laplace-Beltrami operator. We show that our approach is competitive with state-of-the-art methods of map refinement and that guided diffusion models provide a promising pathway to functional map processing.

Figures

Figures reproduced from arXiv: 2506.14322 by the authors.

Figure 1
Figure 1. FRIDU refines computed functional maps (Initial) via a guided image diffusion process.We show refinement results across different datasets and descriptor types: (top) FAUST with WKS, (middle) human TACO with SHOT, and (bottom) non-human TACO with WKS. Left: Error curves demonstrating consistent and significant improvement in correspondence quality. Middle: representative mappings from source to target shapes using t… view at source ↗
Figure 2
Figure 2. An illustration of our (left) training and (right) inference procedures. During training, our pipeline takes as input a random patch of a noisy functional mapCeN i j , conditioned on a corresponding patch of an initial functional mapCei j and position maps, and outputs the denoised patch of the functional map Ci j. At inference time, the patch covers the full-sized image, and we incorporate guidance at each denoisin… view at source ↗
Figure 3
Figure 3. Refinement without Guidance (WKS). We map a func￾tion f1 ∈ R n1 defined on M1 to M2, using the initial, FRIDU re￾fined, and ground-truth functional maps. Here, we map the func￾tion Φ † 1 f1 using the functional map matrices to get ˜f2, and show Φ2 ˜f2 on M2. Note that in this figure only, our refined map does not include guidance in inference, in order to isolate and illustrate the refinement ability of the base mod… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Refinement with P2P Guidance (WKS). We show the pointwise mapping Π21 extracted from the functional maps in Fig￾ure 3, mapping a function f1 ∈ R n1 from M1 to M2 using Π21 f1. We show the pointwise maps obtained without guidance (center) and with P2P guidance (right). …
Figure 5
Figure 5. Figure 5: Functional Map Refinement (SHOT). We show the per￾formance of the pointwise mapping extracted from our refined map alongside the initial and ground-truth mappings. We additionally show the corresponding functional map matrices. Note the noisy appearance of the SHOT-bas…
Figure 7
Figure 7. Figure 7: presents the normalized Euclidean error over the test dataset for both the regular and landmark-constrained settings. For each setting, we show the error for the initial and refined extracted pointwise maps. We observe that the landmark-constrained setting improves bot…
Figure 6
Figure 6. Figure 6: Landmarks. We show results for three settings: (top) training and evaluating on WKS-based initial maps, (center) train￾ing and evaluating on WKS-based initial maps with landmarks, and (bottom) training on WKS-based initial maps and evaluating on WKS-based initial maps …
Figure 8
Figure 8. Figure 8: Comparison to ZoomOut. For the three initial map com￾putation methods presented in Section 3.1, we show a comparison to the ZoomOut axiomatic refinement algorithm [MRR∗ 19] on the Michael dataset. We show results with and without incorporating spectral upsampling durin…
Figure 9
Figure 9. Figure 9: A qualitative example comparing the map improvement obtained by our approach and by DiffZo [MO24]. For a pair of shapes from SHREC19, we show the resulting maps when both methods were trained on Faust (third column in [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: Zero-Shot Condition Generalization We test zero-shot condition generalization of our model by refining SHOT-based maps using a model trained on WKS-based maps, and vice versa, without additional training. The top two rows show the refinement of WKS-based maps using th…
Figure 11
Figure 11. Figure 11: Guidance Parameters. Ablation study of the guidance parameters m, k, and s. Top row: the source function on M1 and its mapping using the initial map and our refined map with our chosen parameters: m = 2, k = 5, s = 500. In the three subsequent rows, we vary each param…

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Works this paper leans on

109 extracted references · 56 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry.original add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 i...

  3. [3]

    : An operator approach to tangent vector field processing

    Azencot O., Ben-Chen M., Chazal F., Ovsjanikov M. : An operator approach to tangent vector field processing. In Computer Graphics Forum (2013), vol. 32, Wiley Online Library, pp. 73--82

    2013

  4. [4]

    : Dpfm: Deep partial functional maps

    Attaiki S., Pai G., Ovsjanikov M. : Dpfm: Deep partial functional maps. In 2021 International Conference on 3D Vision (3DV) (2021), IEEE, pp. 175--185

    2021

  5. [5]

    : The wave kernel signature: A quantum mechanical approach to shape analysis

    Aubry M., Schlickewei U., Cremers D. : The wave kernel signature: A quantum mechanical approach to shape analysis. In 2011 IEEE international conference on computer vision workshops (ICCV workshops) (2011), IEEE, pp. 1626--1633

    2011

  6. [6]

    : Scape: shape completion and animation of people

    Anguelov D., Srinivasan P., Koller D., Thrun S., Rodgers J., Davis J. : Scape: shape completion and animation of people. In ACM Siggraph 2005 Papers. 2005, pp. 408--416

    2005

  7. [7]

    : Universal guidance for diffusion models

    Bansal A., Chu H.-M., Schwarzschild A., Sengupta S., Goldblum M., Geiping J., Goldstein T. : Universal guidance for diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 843--852

    2023

  8. [8]

    : On unsupervised partial shape correspondence

    Bracha A., Dag \`e s T., Kimmel R. : On unsupervised partial shape correspondence. In Proceedings of the Asian Conference on Computer Vision (2024), pp. 4488--4504

    2024

Show all 109 references
  1. [9]

    A., Adeli E., Altman R., Arora S., von Arx S., Bernstein M

    Bommasani R., Hudson D. A., Adeli E., Altman R., Arora S., von Arx S., Bernstein M. S., Bohg J., Bosselut A., Brunskill E., Brynjolfsson E., Buch S., Card D., Castellon R., Chatterji N. S., Chen A. S., Creel K. A., Davis J., Demszky D., Donahue C., Doumbouya M., Durmus E., Erm...

    2021

  2. [10]

    Bogo F., Romero J., Loper M., Black M. J. : Faust: Dataset and evaluation for 3d mesh registration. In Proceedings of the IEEE conference on computer vision and pattern recognition (2014), pp. 3794--3801

    2014

  3. [11]

    : Hybrid functional maps for crease-aware non-isometric shape matching

    Bastian L., Xie Y., Navab N., L \"a hner Z. : Hybrid functional maps for crease-aware non-isometric shape matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), pp. 3313--3323

    2024

  4. [12]

    : Unsupervised deep multi-shape matching

    Cao D., Bernard F. : Unsupervised deep multi-shape matching. In European Conference on Computer Vision (2022), Springer, pp. 55--71

    2022

  5. [13]

    : Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction

    Chen H., Gu J., Chen A., Tian W., Tu Z., Liu L., Su H. : Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction. In Proceedings of the IEEE/CVF international conference on computer vision (2023), pp. 2416--2425

    2023

  6. [14]

    T., Klasky M

    Chung H., Kim J., Mccann M. T., Klasky M. L., Ye J. C. : Diffusion posterior sampling for general noisy inverse problems. arXiv preprint arXiv:2209.14687 (2022)

    2022 arXiv

  7. [15]

    Chung H., Lee S., Ye J. C. : Decomposed diffusion sampler for accelerating large-scale inverse problems. arXiv preprint arXiv:2303.05754 (2023)

    2023 arXiv

  8. [16]

    : Unsupervised learning of robust spectral shape matching

    Cao D., Roetzer P., Bernard F. : Unsupervised learning of robust spectral shape matching. arXiv preprint arXiv:2304.14419 (2023)

    2023 arXiv

  9. [17]

    Chung H., Sim B., Ryu D., Ye J. C. : Improving diffusion models for inverse problems using manifold constraints. Advances in Neural Information Processing Systems 35 (2022), 25683--25696

    2022

  10. [18]

    : Mvsplat360: Feed-forward 360 scene synthesis from sparse views

    Chen Y., Zheng C., Xu H., Zhuang B., Vedaldi A., Cham T.-J., Cai J. : Mvsplat360: Feed-forward 360 scene synthesis from sparse views. arXiv preprint arXiv:2411.04924 (2024)

    2024 arXiv

  11. [19]

    : Deep orientation-aware functional maps: Tackling symmetry issues in shape matching

    Donati N., Corman E., Ovsjanikov M. : Deep orientation-aware functional maps: Tackling symmetry issues in shape matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022), pp. 742--751

    2022

  12. [20]

    S., Muralikrishnan S., Mitra N

    Dutt N. S., Muralikrishnan S., Mitra N. J. : Diffusion 3d features (diff3f): Decorating untextured shapes with distilled semantic features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), pp. 4494--4504

    2024

  13. [21]

    : Diffusion models beat gans on image synthesis

    Dhariwal P., Nichol A. : Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34 (2021), 8780--8794

    2021

  14. [22]

    : Deep geometric functional maps: Robust feature learning for shape correspondence

    Donati N., Sharma A., Ovsjanikov M. : Deep geometric functional maps: Robust feature learning for shape correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 8592--8601

    2020

  15. [23]

    M., Zhang J

    Deng B., Yao Y., Dyke R. M., Zhang J. : A survey of non-rigid 3d registration. In Computer Graphics Forum (2022), vol. 41, Wiley Online Library, pp. 559--589

    2022

  16. [24]

    : Deblurring and denoising of maps between shapes

    Ezuz D., Ben-Chen M. : Deblurring and denoising of maps between shapes. In Computer Graphics Forum (2017), vol. 36, Wiley Online Library, pp. 165--174

    2017

  17. [25]

    : Smooth shells: Multi-scale shape registration with functional maps

    Eisenberger M., Lahner Z., Cremers D. : Smooth shells: Multi-scale shape registration with functional maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 12265--12274

    2020

  18. [26]

    : Neuromorph: Unsupervised shape interpolation and correspondence in one go

    Eisenberger M., Novotny D., Kerchenbaum G., Labatut P., Neverova N., Cremers D., Vedaldi A. : Neuromorph: Unsupervised shape interpolation and correspondence in one go. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021), pp. 7473--7483

    2021

  19. [27]

    : Reversible harmonic maps between discrete surfaces

    Ezuz D., Solomon J., Ben-Chen M. : Reversible harmonic maps between discrete surfaces. ACM Transactions on Graphics (ToG) 38, 2 (2019), 1--12

    2019

  20. [28]

    : Deep shells: Unsupervised shape correspondence with optimal transport

    Eisenberger M., Toker A., Leal-Taix \'e L., Cremers D. : Deep shells: Unsupervised shape correspondence with optimal transport. Advances in Neural information processing systems 33 (2020), 10491--10502

    2020

  21. [29]

    : Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models

    Fan Y., Watkins O., Du Y., Liu H., Ryu M., Boutilier C., Abbeel P., Ghavamzadeh M., Lee K., Lee K. : Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models. Advances in Neural Information Processing Systems 36 (2023), 79858--79885

    2023

  22. [30]

    : Diffusion models as plug-and-play priors

    Graikos A., Malkin N., Jojic N., Samaras D. : Diffusion models as plug-and-play priors. Advances in Neural Information Processing Systems 35 (2022), 14715--14728

    2022

  23. [31]

    : 3dgen: Triplane latent diffusion for textured mesh generation

    Gupta A., Xiong W., Nie Y., Jones I., O g uz B. : 3dgen: Triplane latent diffusion for textured mesh generation. arXiv preprint arXiv:2303.05371 (2023)

    2023 arXiv

  24. [32]

    : Gvgen: Text-to-3d generation with volumetric representation

    He X., Chen J., Peng S., Huang D., Li Y., Huang X., Yuan C., Ouyang W., He T. : Gvgen: Text-to-3d generation with volumetric representation. In European Conference on Computer Vision (2024), Springer, pp. 463--479

    2024

  25. [33]

    : Denoising diffusion probabilistic models

    Ho J., Jain A., Abbeel P. : Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840--6851

    2020

  26. [34]

    : Neural wavelet-domain diffusion for 3d shape generation

    Hui K.-H., Li R., Hu J., Fu C.-W. : Neural wavelet-domain diffusion for 3d shape generation. In SIGGRAPH Asia 2022 Conference Papers (2022), pp. 1--9

    2022

  27. [35]

    M., Kimmel R

    Halimi O., Litany O., Rodola E., Bronstein A. M., Kimmel R. : Unsupervised learning of dense shape correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

    2019

  28. [36]

    M., Kimmel R

    Halimi O., Litany O., Rodola E., Bronstein A. M., Kimmel R. : Unsupervised learning of dense shape correspondence. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 4370--4379

    2019

  29. [37]

    Z., Salakhutdinov R., et al

    He Y., Murata N., Lai C.-H., Takida Y., Uesaka T., Kim D., Liao W.-H., Mitsufuji Y., Kolter J. Z., Salakhutdinov R., et al. : Manifold preserving guided diffusion. arXiv preprint arXiv:2311.16424 (2023)

    2023 arXiv

  30. [38]

    : Classifier-free diffusion guidance

    Ho J., Salimans T. : Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)

    2022 arXiv

  31. [39]

    : An elastic basis for spectral shape correspondence

    Hartwig F., Sassen J., Azencot O., Rumpf M., Ben-Chen M. : An elastic basis for spectral shape correspondence. In ACM SIGGRApH 2023 conference proceedings (2023), pp. 1--11

    2023

  32. [40]

    : Robust symmetry detection via riemannian langevin dynamics

    Je J., Liu J., Yang G., Deng B., Cai S., Wetzstein G., Litany O., Guibas L. : Robust symmetry detection via riemannian langevin dynamics. In SIGGRAPH Asia 2024 Conference Papers (2024), pp. 1--11

    2024

  33. [41]

    : Elucidating the design space of diffusion-based generative models

    Karras T., Aittala M., Aila T., Laine S. : Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems 35 (2022), 26565--26577

    2022

  34. [42]

    : Denoising diffusion restoration models

    Kawar B., Elad M., Ermon S., Song J. : Denoising diffusion restoration models. Advances in Neural Information Processing Systems 35 (2022), 23593--23606

    2022

  35. [43]

    : Learning multi-resolution functional maps with spectral attention for robust shape matching

    Li L., Donati N., Ovsjanikov M. : Learning multi-resolution functional maps with spectral attention for robust shape matching. Advances in Neural Information Processing Systems 35 (2022), 29336--29349

    2022

  36. [44]

    : Repaint: Inpainting using denoising diffusion probabilistic models

    Lugmayr A., Danelljan M., Romero A., Yu F., Timofte R., Van Gool L. : Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2022), pp. 11461--11471

    2022

  37. [45]

    J., Nowrouzezahrai D., Paull L., Liu W

    Liu Z., Feng Y., Black M. J., Nowrouzezahrai D., Paull L., Liu W. : Meshdiffusion: Score-based generative 3d mesh modeling. arXiv preprint arXiv:2303.08133 (2023)

    2023 arXiv

  38. [46]

    : Wonder3d: Single image to 3d using cross-domain diffusion

    Long X., Guo Y.-C., Lin C., Liu Y., Dou Z., Liu L., Ma Y., Zhang S.-H., Habermann M., Theobalt C., et al. : Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2024), pp. 9970--9980

    2024

  39. [47]

    : Diffusion probabilistic models for 3d point cloud generation

    Luo S., Hu W. : Diffusion probabilistic models for 3d point cloud generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2021), pp. 2837--2845

    2021

  40. [48]

    Liu H.-T. D. : Blender Toolbox , 12 2018. URL: https://github.com/HTDerekLiu/BlenderToolbox

    2018

  41. [49]

    D., Jacobson A., Ovsjanikov M

    Liu H.-T. D., Jacobson A., Ovsjanikov M. : Spectral coarsening of geometric operators. arXiv preprint arXiv:1905.05161 (2019)

    2019 arXiv

  42. [50]

    : Shape correspondence using anisotropic chebyshev spectral cnns

    Li Q., Liu S., Hu L., Liu X. : Shape correspondence using anisotropic chebyshev spectral cnns. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (2020), pp. 14658--14667

    2020

  43. [51]

    : Non-rigid puzzles

    Litany O., Rodol \`a E., Bronstein A., Bronstein M., Cremers D. : Non-rigid puzzles. In Computer Graphics Forum (2016), vol. 35, The Eurographics Association and John Wiley & Sons Ltd., pp. 135--143

    2016

  44. [52]

    M., Bronstein M

    Litany O., Rodol \`a E., Bronstein A. M., Bronstein M. M. : Fully spectral partial shape matching. In Computer Graphics Forum (2017), vol. 36, The Eurographics Association and John Wiley & Sons Ltd

    2017

  45. [53]

    : Deep functional maps: Structured prediction for dense shape correspondence

    Litany O., Remez T., Rodola E., Bronstein A., Bronstein M. : Deep functional maps: Structured prediction for dense shape correspondence. In Proceedings of the IEEE international conference on computer vision (2017), pp. 5659--5667

    2017

  46. [54]

    Lukoianov A., S \'a ez de Oc \'a riz Borde H., Greenewald K., Guizilini V., Bagautdinov T., Sitzmann V., Solomon J. M. : Score distillation via reparametrized ddim. Advances in Neural Information Processing Systems 37 (2024), 26011--26044

    2024

  47. [55]

    C., et al

    Lan Y., Tan F., Qiu D., Xu Q., Genova K., Huang Z., Fanello S., Pandey R., Funkhouser T., Loy C. C., et al. : Gaussian3diff: 3d gaussian diffusion for 3d full head synthesis and editing. arXiv preprint arXiv:2312.03763 (2023)

    2023 arXiv

  48. [56]

    : Gaussiandiffusion: 3d gaussian splatting for denoising diffusion probabilistic models with structured noise

    Li X., Wang H., Tseng K.-K. : Gaussiandiffusion: 3d gaussian splatting for denoising diffusion probabilistic models with structured noise. arXiv preprint arXiv:2311.11221 (2023)

    2023 arXiv

  49. [57]

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

    Liu R., Wu R., Van Hoorick B., Tokmakov P., Zakharov S., Vondrick C. : Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision (2023), pp. 9298--9309

    2023

  50. [58]

    : Novelgs: Consistent novel-view denoising via large gaussian reconstruction model, 2024

    Liu J., Xu J., Cheng W., Gao Y., Wang X., Shan Y., Tang Y. : Novelgs: Consistent novel-view denoising via large gaussian reconstruction model, 2024. URL: https://arxiv.org/abs/2411.16779, http://arxiv.org/abs/2411.16779 arXiv:2411.16779

    2024 arXiv

  51. [59]

    : pyfm: Functional maps in python

    Magnet R. : pyfm: Functional maps in python. https://github.com/RobinMagnet/pyFM, 2021. Accessed: 2024-04-10

    2021

  52. [60]

    : Pc2: Projection-conditioned point cloud diffusion for single-image 3d reconstruction

    Melas-Kyriazi L., Rupprecht C., Vedaldi A. : Pc2: Projection-conditioned point cloud diffusion for single-image 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 12923--12932

    2023

  53. [61]

    : Shrec’19: matching humans with different connectivity

    Melzi S., Marin R., Rodol \`a E., Castellani U., Ren J., Poulenard A., Ovsjanikov P., et al. : Shrec’19: matching humans with different connectivity. In Eurographics Workshop on 3D Object Retrieval (2019), The Eurographics Association, pp. 1--8

    2019

  54. [62]

    : Memory-scalable and simplified functional map learning

    Magnet R., Ovsjanikov M. : Memory-scalable and simplified functional map learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), pp. 4041--4050

    2024

  55. [63]

    : Zoomout: Spectral upsampling for efficient shape correspondence

    Melzi S., Ren J., Rodola E., Sharma A., Wonka P., Ovsjanikov M. : Zoomout: Spectral upsampling for efficient shape correspondence. arXiv preprint arXiv:1904.07865 (2019)

    2019 arXiv

  56. [64]

    : Smooth non-rigid shape matching via effective dirichlet energy optimization

    Magnet R., Ren J., Sorkine-Hornung O., Ovsjanikov M. : Smooth non-rigid shape matching via effective dirichlet energy optimization. In 2022 International Conference on 3D Vision (3DV) (2022), IEEE, pp. 495--504

    2022

  57. [65]

    R., Kontschieder P., Nie ner M

    M \"u ller N., Siddiqui Y., Porzi L., Bulo S. R., Kontschieder P., Nie ner M. : Diffrf: Rendering-guided 3d radiance field diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 4328--4338

    2023

  58. [66]

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

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

    2022 arXiv

  59. [67]

    : Improved functional mappings via product preservation

    Nogneng D., Melzi S., Rodola E., Castellani U., Bronstein M., Ovsjanikov M. : Improved functional mappings via product preservation. In Computer Graphics Forum (2018), vol. 37, Wiley Online Library, pp. 179--190

    2018

  60. [68]

    : Functional maps: a flexible representation of maps between shapes

    Ovsjanikov M., Ben-Chen M., Solomon J., Butscher A., Guibas L. : Functional maps: a flexible representation of maps between shapes. ACM Transactions on Graphics (ToG) 31, 4 (2012), 1--11

    2012

  61. [69]

    : Computing and processing correspondences with functional maps

    Ovsjanikov M., Corman E., Bronstein M., Rodol \`a E., Ben-Chen M., Guibas L., Chazal F., Bronstein A. : Computing and processing correspondences with functional maps. In SIGGRAPH ASIA 2016 Courses. 2016, pp. 1--60

    2016

  62. [70]

    T., Mildenhall B

    Poole B., Jain A., Barron J. T., Mildenhall B. : Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988 (2022)

    2022 arXiv

  63. [71]

    : Taco: a benchmark for connectivity-invariance in shape correspondence

    Pedico S., Melzi S., Maggioli F. : Taco: a benchmark for connectivity-invariance in shape correspondence. In Smart Tools and Applications in Graphics-Eurographics Italian Chapter Conference (2024)

    2024

  64. [72]

    : Fast sinkhorn filters: Using matrix scaling for non-rigid shape correspondence with functional maps

    Pai G., Ren J., Melzi S., Wonka P., Ovsjanikov M. : Fast sinkhorn filters: Using matrix scaling for non-rigid shape correspondence with functional maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021), pp. 384--393

    2021

  65. [73]

    : A lesson in splats: Teacher-guided diffusion for 3d gaussian splats generation with 2d supervision, 2024

    Peng C., Sobol I., Tomizuka M., Keutzer K., Xu C., Litany O. : A lesson in splats: Teacher-guided diffusion for 3d gaussian splats generation with 2d supervision, 2024. URL: https://arxiv.org/abs/2412.00623, http://arxiv.org/abs/2412.00623 arXiv:2412.00623

    2024 arXiv

  66. [74]

    : High-resolution image synthesis with latent diffusion models

    Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B. : High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2022), pp. 10684--10695

    2022

  67. [75]

    M., Torsello A., Cremers D

    Rodol \`a E., Cosmo L., Bronstein M. M., Torsello A., Cremers D. : Partial functional correspondence. In Computer graphics forum (2017), vol. 36, Wiley Online Library, pp. 222--236

    2017

  68. [76]

    B., Yuan Y., Kitani K., Kreis K., Fidler S., Litany O

    Rempe D., Luo Z., Peng X. B., Yuan Y., Kitani K., Kreis K., Fidler S., Litany O. : Trace and pace: Controllable pedestrian animation via guided trajectory diffusion. In Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    2023

  69. [77]

    : Discrete optimization for shape matching

    Ren J., Melzi S., Wonka P., Ovsjanikov M. : Discrete optimization for shape matching. In Computer Graphics Forum (2021), vol. 40, Wiley Online Library, pp. 81--96

    2021

  70. [78]

    M., Ovsjanikov M., Azencot O., Ben-Chen M., Chazal F., Guibas L

    Rustamov R. M., Ovsjanikov M., Azencot O., Ben-Chen M., Chazal F., Guibas L. : Map-based exploration of intrinsic shape differences and variability. ACM Transactions on Graphics (TOG) 32, 4 (2013), 1--12

    2013

  71. [79]

    : Continuous and orientation-preserving correspondences via functional maps

    Ren J., Poulenard A., Wonka P., Ovsjanikov M. : Continuous and orientation-preserving correspondences via functional maps. ACM Transactions on Graphics (ToG) 37, 6 (2018), 1--16

    2018

  72. [80]

    : Structured regularization of functional map computations

    Ren J., Panine M., Wonka P., Ovsjanikov M. : Structured regularization of functional map computations. In Computer Graphics Forum (2019), vol. 38, Wiley Online Library, pp. 39--53

    2019

  73. [81]

    : Diffusionnet: Discretization agnostic learning on surfaces

    Sharp N., Attaiki S., Crane K., Ovsjanikov M. : Diffusionnet: Discretization agnostic learning on surfaces. ACM Transactions on Graphics (TOG) 41, 3 (2022), 1--16

    2022

  74. [82]

    R., Chan E

    Shue J. R., Chan E. R., Po R., Ankner Z., Wu J., Wetzstein G. : 3d neural field generation using triplane diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 20875--20886

    2023

  75. [83]

    : Deep unsupervised learning using nonequilibrium thermodynamics

    Sohl-Dickstein J., Weiss E., Maheswaranathan N., Ganguli S. : Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning (2015), pmlr, pp. 2256--2265

    2015

  76. [84]

    : Generative modeling by estimating gradients of the data distribution

    Song Y., Ermon S. : Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32 (2019)

    2019

  77. [85]

    : Spatially and spectrally consistent deep functional maps, 2023

    Sun M., Mao S., Jiang P., Ovsjanikov M., Huang R. : Spatially and spectrally consistent deep functional maps, 2023. URL: https://arxiv.org/abs/2308.08871, http://arxiv.org/abs/2308.08871 arXiv:2308.08871

    2023 arXiv

  78. [86]

    : Spatially and spectrally consistent deep functional maps

    Sun M., Mao S., Jiang P., Ovsjanikov M., Huang R. : Spatially and spectrally consistent deep functional maps. In Proceedings of the IEEE/CVF International Conference on Computer Vision (2023), pp. 14497--14507

    2023

  79. [87]

    : Viewset diffusion: (0-)image-conditioned 3d generative models from 2d data

    Szymanowicz S., Rupprecht C., Vedaldi A. : Viewset diffusion: (0-)image-conditioned 3d generative models from 2d data. International Conference on Computer Vision (2023)

    2023

  80. [88]

    : Shot: Unique signatures of histograms for surface and texture description

    Salti S., Tombari F., Di Stefano L. : Shot: Unique signatures of histograms for surface and texture description. Computer vision and image understanding 125 (2014), 251--264

    2014

  81. [89]

    : Zero-to-hero: Enhancing zero-shot novel view synthesis via attention map filtering

    Sobol I., Xu C., Litany O. : Zero-to-hero: Enhancing zero-shot novel view synthesis via attention map filtering. Advances in Neural Information Processing Systems 37 (2024), 30522--30553

    2024

  82. [90]

    : Loss-guided diffusion models for plug-and-play controllable generation

    Song J., Zhang Q., Yin H., Mardani M., Liu M.-Y., Kautz J., Chen Y., Vahdat A. : Loss-guided diffusion models for plug-and-play controllable generation. In International Conference on Machine Learning (2023), PMLR, pp. 32483--32498

    2023

  83. [91]

    : Shape registration in the time of transformers

    Trappolini G., Cosmo L., Moschella L., Marin R., Melzi S., Rodol \`a E. : Shape registration in the time of transformers. Advances in Neural Information Processing Systems 34 (2021), 5731--5744

    2021

  84. [92]

    : Volumediffusion: Flexible text-to-3d generation with efficient volumetric encoder

    Tang Z., Gu S., Wang C., Zhang T., Bao J., Chen D., Guo B. : Volumediffusion: Flexible text-to-3d generation with efficient volumetric encoder. arXiv preprint arXiv:2312.11459 (2023)

    2023 arXiv

  85. [93]

    P., Hariharan B

    Tang L., Jia M., Wang Q., Phoo C. P., Hariharan B. : Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems 36 (2023), 1363--1389

    2023

  86. [94]

    : Dreamgaussian: Generative gaussian splatting for efficient 3d content creation

    Tang J., Ren J., Zhou H., Liu Z., Zeng G. : Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653 (2023)

    2023 arXiv

  87. [95]

    : Unique signatures of histograms for local surface description

    Tombari F., Salti S., Di Stefano L. : Unique signatures of histograms for local surface description. In Computer Vision--ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part III 11 (2010), Springer, pp. 356--369

    2010

  88. [96]

    : Steklov spectral geometry for extrinsic shape analysis

    Wang Y., Ben-Chen M., Polterovich I., Solomon J. : Steklov spectral geometry for extrinsic shape analysis. ACM Transactions on Graphics (TOG) 38, 1 (2018), 1--21

    2018

  89. [97]

    : Novel view synthesis with diffusion models, 2022

    Watson D., Chan W., Martin-Brualla R., Ho J., Tagliasacchi A., Norouzi M. : Novel view synthesis with diffusion models, 2022. URL: https://arxiv.org/abs/2210.04628, http://arxiv.org/abs/2210.04628 arXiv:2210.04628

    2022 arXiv

  90. [98]

    : Patch diffusion: Faster and more data-efficient training of diffusion models

    Wang Z., Jiang Y., Zheng H., Wang P., He P., Wang Z., Chen W., Zhou M., et al. : Patch diffusion: Faster and more data-efficient training of diffusion models. Advances in neural information processing systems 36 (2023), 72137--72154

    2023

  91. [99]

    : Sketch and text guided diffusion model for colored point cloud generation

    Wu Z., Wang Y., Feng M., Xie H., Mian A. : Sketch and text guided diffusion model for colored point cloud generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (2023), pp. 8929--8939

    2023

  92. [100]

    : Zero-shot image restoration using denoising diffusion null-space model

    Wang Y., Yu J., Zhang J. : Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490 (2022)

    2022 arXiv

  93. [101]

    : Amodal3r: Amodal 3d reconstruction from occluded 2d images

    Wu T., Zheng C., Guan F., Vedaldi A., Cham T.-J. : Amodal3r: Amodal 3d reconstruction from occluded 2d images. arXiv preprint arXiv:2503.13439 (2025)

    2025 arXiv

  94. [102]

    : Rodin: A generative model for sculpting 3d digital avatars using diffusion

    Wang T., Zhang B., Zhang T., Gu S., Bao J., Baltrusaitis T., Shen J., Chen D., Wen F., Chen Q., et al. : Rodin: A generative model for sculpting 3d digital avatars using diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2023), pp....

    2023

  95. [103]

    : 3difftection: 3d object detection with geometry-aware diffusion features

    Xu C., Ling H., Fidler S., Litany O. : 3difftection: 3d object detection with geometry-aware diffusion features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), pp. 10617--10627

    2024

  96. [104]

    : Structured 3d latents for scalable and versatile 3d generation

    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. arXiv preprint arXiv:2412.01506 (2024)

    2024 arXiv

  97. [105]

    : Freedom: Training-free energy-guided conditional diffusion model

    Yu J., Wang Y., Zhao C., Ghanem B., Zhang J. : Freedom: Training-free energy-guided conditional diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision (2023), pp. 23174--23184

    2023

  98. [106]

    : Rodinhd: High-fidelity 3d avatar generation with diffusion models

    Zhang B., Cheng Y., Wang C., Zhang T., Yang J., Tang Y., Zhao F., Chen D., Guo B. : Rodinhd: High-fidelity 3d avatar generation with diffusion models. In European Conference on Computer Vision (2024), Springer, pp. 465--483

    2024

  99. [107]

    : Gaussiancube: A structured and explicit radiance representation for 3d generative modeling

    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. arXiv preprint arXiv:2403.19655 (2024)

    2024 arXiv

  100. [108]

    : Point cloud pre-training with diffusion models

    Zheng X., Huang X., Mei G., Hou Y., Lyu Z., Dai B., Ouyang W., Gong Y. : Point cloud pre-training with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024), pp. 22935--22945

    2024

  101. [109]

    : Denoising functional maps: Diffusion models for shape correspondence

    Zhuravlev A., L \"a hner Z., Golyanik V. : Denoising functional maps: Diffusion models for shape correspondence. arXiv preprint arXiv:2503.01845 (2025)

    2025 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.