Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that single-image 3D portraits become blurry because viewpoints are conditioned and noised inconsistently, and that a hybrid-prior conditioning network plus a multi-view noise resampling strategy produces sharp…

desk verdict A plausible pipeline with a genuinely interesting noise-resampling idea, but the missing appendix and unvalidated invariance assumption make the central claim hard to trust yet. read the letter →

arxiv 2411.10369 v1 pith:O7ZTFWUO submitted 2024-11-15 cs.CV cs.AI

Haoran Wei , Wencheng Han , Xingping Dong , Jianbing Shen This is my paper · ORCID
classification cs.CVcs.AI
keywords single-image3Dportraitgenerationdiffusionmodelsscoredistillationsamplingmulti-viewconsistencytri-planeNeRFnoiseresamplingtexturefidelityGANpriorinitialization
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

Recent diffusion-based methods generate 3D portraits from a single image by distilling 2D diffusion priors into a 3D representation, but the resulting textures are often over-smoothed. This paper argues that the cause is cross-view inconsistency in the diffusion process itself: each viewpoint is conditioned through implicit attention only, and each viewpoint's diffusion noise is sampled independently, so the SDS gradients conflict and the optimizer averages them away. To fix this, the authors propose Portrait Diffusion, which couples viewpoints in conditioning (an explicit point-cloud-projected reference plus an implicit texture/geometry branch) and in the noise domain (a multi-view noise resampling strategy that aligns each view's noise with the driving view). The paper reports that this yields 3D portraits with accurate geometry and rich details from a single image, with quantitative results on CLIP-I, LPIPS, and identity score better than the compared methods. If the claim holds, the bottleneck for one-shot 3D portrait generation is not the 3D representation but the stochasticity management across views.

What carries the argument

The load-bearing mechanism is the Multi-View Noise Resampling Strategy (MV-NRS) embedded in the SDS loss. It first creates an anchor noise for each viewpoint by lifting the driving view's noise into a colored point cloud using the rendered depth, then re-rendering that point cloud at the target viewpoint to obtain $\epsilon^{\mathrm{Ac}}_{v_{i+1},0}$; any voids are filled with fresh random noise, so the anchor noise itself is a cross-view prior. During optimization, at each iteration the method samples a low-variance noise $\epsilon^{\mathrm{Rs}}$ around the anchor and computes denoising gradients for both the resampled and anchor noises; it keeps the resample only if the cosine similarity between its gradient and the driving view's gradient exceeds that of the anchor noise. This gradient-consistency gate (Eqs. 14–17) progressively aligns the multi-view representations. Around this sits the Hybrid Priors Diffusion Model (HPDM), a two-branch conditioning network: an explicit branch ingests the point-cloud-projected reference, segmentation mask, rendered image, and normal map through zero-initialized convolutions, and an implicit branch transfers texture latents from the driving view through residual blocks, so the diffusion condition is itself cross-view consistent.

What would settle it

Render a fixed 3D portrait at two views separated by 30°, 90°, and 180° in azimuth, and compute the cosine similarity between the SDS denoising gradients obtained with the MV-NRS anchor noise and with independent random noise. The paper's mechanism predicts the anchor noise keeps gradient similarity substantially higher than the random baseline at all separations; if the similarity collapses to the random level beyond 60°–90°, the invariance assumption fails and the reported texture gains cannot be attributed to cross-view noise alignment.

Watch

Extended reading notes

Core claim

The central discovery, stated by the authors, is that blurry textures in single-image 3D portrait generation are caused by two forms of cross-view inconsistency that prior SDS-based methods do not address: inconsistency of 'status' (the conditioning that guides novel views) and inconsistency of 'representation' (the diffusion noise that drives each view's sampling). The paper asserts that adding explicit geometric priors through point-cloud projection and implicit texture/geometry priors through residual feature transfer fixes the status inconsistency, while a Multi-View Noise Resampling Strategy fixes the representation inconsistency by constructing a shared anchor noise per viewpoint and iteratively resampling it toward gradient consistency with the driving view. As evidence, the paper reports the highest CLIP-I (0.9986), lowest LPIPS (0.3616), and highest identity score (0.3440) among Portrait3D, Wonder3D, DreamCraft3D, and the proposed method, with qualitative results showing visible hair-strand detail.

Load-bearing premise

The argument rests on the assumption that a 2D diffusion model treats a noise field warped through a point cloud as equivalent to sampling at the target view—invariance to small local transformations—so that the anchor noise actually aligns the per-view denoising distributions; if that invariance breaks at the large viewpoint angles used in training, the resampling strategy starts from a misaligned anchor and can reinforce artifacts.

Editorial extensions

If this is right

  • SDS-based 3D generation can be made sharper by treating the diffusion noise distribution as a cross-view object to be aligned, not as an independent draw per viewpoint.
  • Explicit geometric projection (point-cloud re-rendering) and implicit feature transfer are complementary: the former gives coarse alignment, the latter repairs projection artifacts, so later methods can adopt the same two-branch conditioning pattern.
  • A single photo suffices for a full 360-degree head model with fine hair detail, assuming the invariance assumption holds across the sampled viewpoints.
  • The reported numbers (CLIP-I 0.9986, LPIPS 0.3616, ID 0.3440) set the comparison point for subsequent single-image 3D portrait methods on the paper's evaluation protocol.

Reading between the lines

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

  • The noise-resampling idea generalizes beyond portraits: any SDS pipeline with a depth-capable renderer could lift noise through geometry to align multi-view gradients, which may reduce texture blur in object- and scene-level 3D generation.
  • If the diffusion model's invariance to warped noise is only local, the method's benefit should shrink at large azimuth separations; a viewpoint-adaptive version that blends anchor and random noise based on overlap could extend it.
  • Because the pipeline stacks GAN initialization, geometry restoration, and diffusion refinement, a clean attribution of the gains would require initializing the baselines from the same tri-plane to isolate the proposed consistency modules.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a three-stage pipeline for single-image 3D portrait generation: GAN-prior initialization, portrait geometry restoration, and multi-view diffusion refinement. The refinement stage contains two main contributions: a Hybrid Priors Diffusion Model (HPDM), which conditions the diffusion UNet with explicit projected references, geometric priors, and implicit texture priors, and a Multi-View Noise Resampling Strategy (MV-NRS), which aligns the diffusion noise across viewpoints by projecting anchor noise through a point cloud and updating it with gradient-consistency scores inside the SDS loss. The authors report qualitative comparisons with Portrait3D, Wonder3D, and DreamCraft3D, and a quantitative table with CLIP-I, LPIPS, and ID scores, concluding that their method produces more detailed and identity-consistent 3D portraits.

Significance. If the reported results are reproducible, the paper addresses a real limitation of SDS-based single-image 3D portrait generation: over-smoothed textures caused by inconsistent multi-view denoising. The idea of controlling the noise distribution rather than only the conditioning signal is a plausible and potentially useful direction, and the explicit-plus-implicit conditioning design is a reasonable engineering contribution. The paper does not rely on fitted metrics or circular derivations; the evaluation metrics are external to the method. However, the current version is a preprint with missing implementation sections, a quantitative evaluation without statistical support, and a central technical assumption that is not experimentally validated. Those issues prevent the findings from being verified as stated.

major comments (4)
  1. [Section 4.3, Table 1] The quantitative evaluation is not sufficiently specified to support the claimed improvements. The paper does not state which dataset or how many identities were evaluated, nor does it report per-identity variance, error bars, or significance tests. The CLIP-I differences are small (0.9956 versus 0.9986), and the LPIPS and ID differences could easily be within noise for small samples. Please specify the evaluation set, report N and standard deviations, and run a paired significance test or confidence interval for each metric.
  2. [Section 3.4, Eq. (13)] MV-NRS rests on the assertion that the 2D diffusion model is invariant to linear transformations and robust to small-scale nonlinear transformations. The construction in Eq. (13) unprojects source-view latent noise through a NeRF depth map and renders it to the target view, which involves occlusion, resampling, and mask-boundary discontinuities, and the training viewpoints span the full 360° azimuth and pitch from -30° to 30° (Section 4.1). This is not obviously a small-range linear transformation, and the paper provides no experiment showing that the projected anchor noise remains approximately Gaussian or in-distribution for the diffusion UNet. The ablation in Fig. 6 is qualitative, uses a single image, and does not measure the distributional validity of the resampled noise or its effect on multi-view gradient consistency. Please add quantitative ablations, for example the distance between projected anchor noise and N(0, I), denoising consistency across adjacent views, and the final metrics with and without MV-NRS, and state the range of viewpoint changes for which the invariance is expected to hold.
  3. [Sections 3.2, 3.3, 3.4, and 4.1] Several load-bearing implementation details are deferred to sections that are not present in this version. The res-block design is said to be detailed in Sec. 6 (after Eq. 10), and the full MV-NRS procedure, hyperparameters, and training details are repeatedly deferred to Sec. 7 (after Eqs. 6 and 13, and in Section 4.1). Without these sections, the pipeline cannot be re-implemented or independently checked. Please include the missing sections or make the full algorithm available in a supplement, including the anchor-noise optimization iterations S, the resampling variance sigma^2, the control weight w_Ex, the tri-plane configuration, and the training schedules for each stage.
  4. [Section 3.4, Eqs. (14)-(17)] The anchor-noise update rule is under-specified. It is not clear how grad^P_{v_i,s} is computed when the paper says it directly uses I_{v_i,s-1} from the previous training iteration, because Eqs. (14)-(15) define a gradient from a denoised image and a rendered image, and it is unclear which images are retained across iterations. It is also not clear how the 'driven view' gradient grad_{v_{i-1},s} is selected when Eq. (18) aggregates gradients across all viewpoints. Please provide a precise algorithmic listing or pseudocode for the resampling retention and anchor update process.
minor comments (5)
  1. [General] The manuscript contains numerous typos and inconsistent terminology, including 'Diffsion' in the abstract, 'Resamplig' in the abstract, 'Mult-view' in the conclusion, 'Consisency' in the Section 3.4 heading, and 'corss-view' in the Figure 3 caption. A careful proofreading pass is needed.
  2. [Eq. (2)] There is a typo in the SDS loss formulation: 'weitghts' should be 'weights'.
  3. [Section 4.3 and Figure 4] The comparison protocol for Portrait3D should be described more precisely. The paper states that Portrait3D is a text-to-3D method and that the authors bypass the text-to-image step by directly providing the reference image; this adapted protocol may affect the comparison fairness and should be explained in more detail.
  4. [References] Two different works are cited as 'Portrait3D' ([13] and [36]), which is confusing. The citations should be disambiguated in the text, for example by using author names or venue labels.
  5. [Figure 3] The caption of Figure 3 refers to 'NV-NRS' instead of 'MV-NRS', making the figure harder to read.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported gains are measured against external benchmarks and no prediction reduces by construction to a fitted input.

full rationale

This is an empirical engineering paper whose central claims are evaluated by CLIP-I, LPIPS, and ID metrics computed on rendered views against the reference image; these metrics are external to the method and are not defined in terms of any fitted parameter. The MV-NRS noise alignment in Eq. (13) is an algorithmic construction rather than a prediction derived from a fitted value, and its effectiveness is tested by ablations in Fig. 6 and by the final external metrics in Table 1. The paper's assertion in Sec. 3.4 that the 2D diffusion model is 'invariance to linear transformations and robust to small-scale nonlinear transformations' is an unverified modeling assumption that could affect robustness, but it is not circular: the anchor noise distribution is not defined as the quantity being predicted, and no equation in the paper reduces the claimed result to its own inputs. Citations to Portrait3D and GAN priors refer to external prior work, and no load-bearing step depends on a self-citation or on an imported uniqueness theorem. The missing implementation details referenced as 'Sec. 7' are a completeness issue, not a circularity issue. Overall, the derivation chain is self-contained against external benchmarks, so the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities. Its free parameters are standard network and optimization hyperparameters, although most are left unspecified.

free parameters (6)
  • control weight wEx = not specified
    Weights the explicit condition branch in Eq. 9; value is chosen by hand, not reported.
  • resampling variance sigma^2 = not specified
    Controls spread of resampled noise around anchor noise in Eq. 12; no value or schedule given.
  • anchor noise optimization iterations S = not specified
    Number of MV-NRS refinement iterations per training step, mentioned in Sec. 3.4 but no value.
  • tri-plane resolution and channels = 128x128, 96 channels
    Architecture choice in Sec. 4.1 affects capacity and detail.
  • number of fixed viewpoints = 13
    Fixed reconstruction viewpoints during texture refinement, Sec. 4.1.
  • camera pitch range = -30 to 30 degrees
    Pitch sampling range for SDS training in geometry restoration, Sec. 4.1.
assumptions (5)
  • domain assumption 2D diffusion outputs are invariant to linear transformations and robust to small nonlinear transformations
    Stated in Sec. 3.4 to justify warping noise through point clouds; no proof or measurement.
  • domain assumption SDS loss with a 2D diffusion prior yields 3D-consistent geometry
    Adopted from DreamFusion Eq. 2; standard in the field but unproven here.
  • domain assumption GAN prior (Portrait4D) provides a useful tri-plane initialization
    Assumed in Sec. 3.2; no analysis of sensitivity to GAN quality.
  • domain assumption CLIP-I, LPIPS, and ID scores on five rendered views are valid proxies for 3D portrait fidelity
    Evaluation design in Sec. 4.3; no correlation with human judgment shown.
  • domain assumption Gradient consistency with the driven view identifies superior noise samples
    Core selection rule in Eq. 16-17; never validated independently.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion." pith.science (2026). https://pith.science/paper/O7ZTFWUO

@misc{pith2026241110369,
  author       = {Pith},
  title        = {Pith review of: Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O7ZTFWUO}},
  note         = {Machine review of arXiv:2411.10369}
}
read the original abstract

Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representations. However, these methods usually struggle to produce high-fidelity 3D models, frequently yielding excessively blurred textures. We attribute this issue to the insufficient consideration of cross-view consistency during the diffusion process, resulting in significant disparities between different views and ultimately leading to blurred 3D representations. In this paper, we address this issue by comprehensively exploiting multi-view priors in both the conditioning and diffusion procedures to produce consistent, detail-rich portraits. From the conditioning standpoint, we propose a Hybrid Priors Diffsion model, which explicitly and implicitly incorporates multi-view priors as conditions to enhance the status consistency of the generated multi-view portraits. From the diffusion perspective, considering the significant impact of the diffusion noise distribution on detailed texture generation, we propose a Multi-View Noise Resamplig Strategy integrated within the optimization process leveraging cross-view priors to enhance representation consistency. Extensive experiments demonstrate that our method can produce 3D portraits with accurate geometry and rich details from a single image. The project page is at \url{https://haoran-wei.github.io/Portrait-Diffusion}.

Figures

Figures reproduced from arXiv: 2411.10369 by the authors.

Figure 1
Figure 1. Our proposed Portrait Diffusion framework can generate high-quality detail-rich 3D portraits from a single reference portrait image. In comparison to SOTA methods Wonder3D [19] and Portrait3D [36], our approach achieves clearer and more detailed textures. Abstract Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then dis… view at source ↗
Figure 2
Figure 2. The Portrait Diffusion Framework. This framework comprises three integral modules. GAN-prior Portrait Initialization, employs existing Portrait GAN priors to derive initial tri-plane NeRF features from frontal-view portrait images. Portrait Geometry Restoration, is focused on reconstructing the geometry using these initialized tri-planes. Multi-view Diffusion Texture Refinement, trans￾forms coarse textures into deta… view at source ↗
Figure 3
Figure 3. The presentations of our proposed Hybrid Priors Portrait Diffusion model (a) and Multi-View Noise Resampling Strategy (b). HPDM is designed to leverage various multi-view priors in a hybrid manner to condition the new view synthetic process for more consistent status. NV-NRS is designed to transfer corss-view priors to control the diffusion noise distribution for representations alignment. and the distribution of no… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison to SOTA approaches: Portrait3D [ [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Visual Results for Ablation study on Multi-View Noise [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. T2I-ConBench: Text-to-Image Benchmark for Continual Post-training

    cs.CV 2025-05 conditional novelty 6.0 of 10

    T2I-ConBench provides a unified multi-metric benchmark for continual post-training of text-to-image models and shows that all tested methods have notable weaknesses.

Reference graph

Works this paper leans on

49 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Ogras, and Linjie Luo

    Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y . Ogras, and Linjie Luo. Panohead: Geometry-aware 3d full- head synthesis in 360deg. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20950–20959, 2023. 1, 3

    work page 2023

  2. [2]

    Mvinpainter: Learning multi-view consis- tent inpainting to bridge 2d and 3d editing

    Chenjie Cao, Chaohui Yu, Yanwei Fu, Fan Wang, and Xi- angyang Xue. Mvinpainter: Learning multi-view consis- tent inpainting to bridge 2d and 3d editing. arXiv preprint arXiv:2408.08000, 2024. 2

    arXiv 2024

  3. [3]

    Chan, Connor Z

    Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, and Sameh Khamis. Efficient geometry-aware 3d generative adversarial net- works. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16123– 16133, 2022. 2

    work page 2022

  4. [4]

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

    Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2416–2425, 2023. 2

    work page 2023

  5. [5]

    Latentpaint: Image inpainting in latent space with diffusion models

    Ciprian Corneanu, Raghudeep Gadde, and Aleix M Mar- tinez. Latentpaint: Image inpainting in latent space with diffusion models. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , pages 4334–4343, 2024. 1

    2024

  6. [6]

    Gram: Generative radiance manifolds for 3d-aware image generation

    Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10673– 10683, 2022. 2

    work page 2022

  7. [7]

    Portrait4d: Learning one-shot 4d head avatar synthesis using synthetic data

    Yu Deng, Duomin Wang, and Baoyuan Wang. Portrait4d: Learning one-shot 4d head avatar synthesis using synthetic data. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, 2024. 1, 4

    work page 2024

  8. [8]

    Portrait4d-v2: Pseudo multi-view data creates better 4d head synthesizer

    Yu Deng, Duomin Wang, and Baoyuan Wang. Portrait4d-v2: Pseudo multi-view data creates better 4d head synthesizer. arXiv preprint arXiv:2403.13570, 2024. 1

    arXiv 2024

Show all 49 references
  1. [9]

    Text-to-3d generation with bidirectional diffusion using both 2d and 3d priors

    Lihe Ding, Shaocong Dong, Zhanpeng Huang, Zibin Wang, Yiyuan Zhang, Kaixiong Gong, Dan Xu, and Tianfan Xue. Text-to-3d generation with bidirectional diffusion using both 2d and 3d priors. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, p...

    2024

  2. [10]

    Headgan: One-shot neural head synthesis and editing

    Michail Christos Doukas, Stefanos Zafeiriou, and Viktoriia Sharmanska. Headgan: One-shot neural head synthesis and editing. In Proceedings of the IEEE/CVF International con- ference on Computer Vision, pages 14398–14407, 2021. 1

    2021

  3. [11]

    Progressive learning of 3d reconstruction network from 2d gan data

    Aysegul Dundar, Jun Gao, Andrew Tao, and Bryan Catan- zaro. Progressive learning of 3d reconstruction network from 2d gan data. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2023. 2

    2023

  4. [12]

    Get3d: A generative model of high quality 3d textured shapes learned from images

    Jingwei Gao, Tianyang Shen, Zhen Wang, Weikai Chen, Kangxue Yin, Difan Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. arXiv preprint arXiv:2209.11163, 2022. 2

    2022 arXiv

  5. [13]

    Portrait3d: 3d head genera- tion from single in-the-wild portrait image

    J Hao, J Tang, J Zhang, et al. Portrait3d: 3d head genera- tion from single in-the-wild portrait image. arXiv preprint arXiv:2406.16710, 2024. 2, 3, 4

    2024 arXiv

  6. [14]

    Mobile- portrait: Real-time one-shot neural head avatars on mobile devices

    Jianwen Jiang, Gaojie Lin, Zhengkun Rong, Chao Liang, Yongming Zhu, Jiaqi Yang, and Tianyun Zhong. Mobile- portrait: Real-time one-shot neural head avatars on mobile devices. arXiv preprint arXiv:2407.05712, 2024. 1

    2024 arXiv

  7. [15]

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

    Xuan Ju, Xian Liu, Xintao Wang, Yuxuan Bian, Ying Shan, and Qiang Xu. Brushnet: A plug-and-play image inpaint- ing model with decomposed dual-branch diffusion. arXiv preprint arXiv:2403.06976, 2024. 5

    2024 arXiv

  8. [16]

    Holodiffusion: Training a 3d diffusion model using 2d images

    Animesh Karnewar, Andrea Vedaldi, David Novotny, and Niloy J Mitra. Holodiffusion: Training a 3d diffusion model using 2d images. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 18423–18433, 2023. 2

    2023

  9. [17]

    Diffusionavatars: Deferred diffusion for high- fidelity 3d head avatars

    Tobias Kirschstein, Simon Giebenhain, and Matthias Nießner. Diffusionavatars: Deferred diffusion for high- fidelity 3d head avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ,

  10. [18]

    Generalizable one-shot 3d neural head avatar

    Xueting Li, Shalini De Mello, Sifei Liu, Koki Nagano, Umar Iqbal, and Jan Kautz. Generalizable one-shot 3d neural head avatar. Advances in Neural Information Processing Systems, 36, 2024. 1

    2024

  11. [19]

    Wonder3d: Sin- gle image to 3d using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Sin- gle image to 3d using cross-domain diffusion. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pa...

    2024

  12. [20]

    Otavatar: One-shot talking face avatar with control- lable tri-plane rendering

    Zhiyuan Ma, Xiangyu Zhu, Guo-Jun Qi, Zhen Lei, and Lei Zhang. Otavatar: One-shot talking face avatar with control- lable tri-plane rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16901–16910, 2023. 1

    2023

  13. [21]

    Reference-guided control- lable inpainting of neural radiance fields

    Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G Derpanis, and Igor Gilitschenski. Reference-guided control- lable inpainting of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on C...

    2023

  14. [22]

    Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields

    Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstanti- nos G Derpanis, Jonathan Kelly, Marcus A Brubaker, Igor Gilitschenski, and Alex Levinshtein. Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields. In Proceedings of the IEEE/CVF Conferen...

    2023

  15. [23]

    Reffusion: Reference adapted dif- fusion models for 3d scene inpainting

    Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim, David Acuna, Jonathan Kelly, Sanja Fidler, Igor Gilitschen- ski, and Zan Gojcic. Reffusion: Reference adapted dif- fusion models for 3d scene inpainting. arXiv preprint arXiv:2404.10765, 2024. 2

    2024 arXiv

  16. [25]

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

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

    2022 arXiv

  17. [26]

    Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors.arXiv preprint arXiv:2306.17843,

    Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Sko- rokhodov, Peter Wonka, Sergey Tulyakov, et al. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors.arXiv preprint arXiv:2306.17843,

    arXiv

  18. [27]

    G3dr: Generative 3d reconstruction in imagenet

    Pradyumna Reddy, Ismail Elezi, and Jiankang Deng. G3dr: Generative 3d reconstruction in imagenet. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9655–9665, 2024. 2

    2024

  19. [28]

    Human4dit: Free-view human video generation with 4d diffusion transformer

    Ruizhi Shao, Youxin Pang, Zerong Zheng, Jingxiang Sun, and Yebin Liu. Human4dit: Free-view human video generation with 4d diffusion transformer. arXiv preprint arXiv:2405.17405, 2024. 2

    2024 arXiv

  20. [29]

    Dreamcraft3d: Hierarchi- cal 3d generation with bootstrapped diffusion prior

    Jingxiang Sun, Bo Zhang, Ruizhi Shao, Lizhen Wang, Wen Liu, Zhenda Xie, and Yebin Liu. Dreamcraft3d: Hierarchi- cal 3d generation with bootstrapped diffusion prior. In The Twelfth International Conference on Learning Representa- tions, 2024. 2, 7

    2024

  21. [30]

    J. Tang, T. Wang, B. Zhang, T. Zhang, R. Yi, L. Ma, and D. Chen. Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior. arXiv preprint arXiv:2303.14184, 2023. 1, 2

    2023 arXiv

  22. [31]

    J. Tang, Y . Zeng, K. Fan, X. Wang, B. Dai, K. Chen, and L. Ma. Make-it-vivid: Dressing your animatable biped cartoon characters from text. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 6243–6253, 2024. 1, 2

    2024

  23. [32]

    Diffusion with forward models: Solv- ing stochastic inverse problems without direct supervision

    Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Fr ´edo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with forward models: Solv- ing stochastic inverse problems without direct supervision. Advances in Neural Information Processing System...

    2023

  24. [33]

    Innerf360: Text-guided 3d-consistent object in- painting on 360-degree neural radiance fields

    Dongqing Wang, Tong Zhang, Alaa Abboud, and Sabine S¨usstrunk. Innerf360: Text-guided 3d-consistent object in- painting on 360-degree neural radiance fields. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12677–12686, 2024. 2

    2024

  25. [34]

    Nerfiller: Completing scenes via generative 3d inpainting

    Ethan Weber, Aleksander Holynski, Varun Jampani, Saurabh Saxena, Noah Snavely, Abhishek Kar, and Angjoo Kanazawa. Nerfiller: Completing scenes via generative 3d inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20731– 20741...

    2024

  26. [35]

    3dportraitgan: Learning one-quarter headshot 3d gans from a single-view portrait dataset with diverse body poses

    Yiqian Wu, Hao Xu, Xiangjun Tang, Hongbo Fu, and Xiao- gang Jin. 3dportraitgan: Learning one-quarter headshot 3d gans from a single-view portrait dataset with diverse body poses. arXiv preprint arXiv:2307.14770, 2023. 1

    2023 arXiv

  27. [36]

    Portrait3d: Text-guided high-quality 3d por- trait generation using pyramid representation and gans prior

    Yiqian Wu et al. Portrait3d: Text-guided high-quality 3d por- trait generation using pyramid representation and gans prior. ACM Transactions on Graphics (TOG), 43(4):1–12, 2024. 1, 3, 7

    2024

  28. [37]

    Gram-hd: 3d-consistent image generation at high resolu- tion with generative radiance manifolds

    Jing Xiang, Jian Yang, Yinda Deng, and Xin Tong. Gram-hd: 3d-consistent image generation at high resolu- tion with generative radiance manifolds. arXiv preprint arXiv:2206.07255, 2022. 2

    2022 arXiv

  29. [38]

    3d-aware image generation using 2d diffusion mod- els

    Jianfeng Xiang, Jiaolong Yang, Binbin Huang, and Xin Tong. 3d-aware image generation using 2d diffusion mod- els. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 2383–2393, 2023. 2

    2023

  30. [39]

    One-shot identity-preserving portrait reenactment

    Sitao Xiang, Yuming Gu, Pengda Xiang, Mingming He, Koki Nagano, Haiwei Chen, and Hao Li. One-shot identity-preserving portrait reenactment. arXiv preprint arXiv:2004.12452, 2020. 1

    2004 arXiv

  31. [40]

    High-fidelity 3d gan inversion by pseudo- multi-view optimization

    Jiaxin Xie, Hao Ouyang, Jingtan Piao, Chenyang Lei, and Qifeng Chen. High-fidelity 3d gan inversion by pseudo- multi-view optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 321–331, 2023. 2

    2023

  32. [41]

    Smartbrush: Text and shape guided object inpainting with diffusion model

    Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, and Kun Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 22428–22437, 2023. 1

    2023

  33. [42]

    X-portrait: Expressive portrait anima- tion with hierarchical motion attention

    You Xie, Hongyi Xu, Guoxian Song, Chao Wang, Yichun Shi, and Linjie Luo. X-portrait: Expressive portrait anima- tion with hierarchical motion attention. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 2

    2024

  34. [43]

    Get3dhuman: Lifting stylegan-human into a 3d generative model using pixel-aligned reconstruction priors

    Zhangyang Xiong, Di Kang, Derong Jin, Weikai Chen, Lin- chao Bao, Shuguang Cui, and Xiaoguang Han. Get3dhuman: Lifting stylegan-human into a 3d generative model using pixel-aligned reconstruction priors. In Proceedings of the IEEE/CVF International Conference on Computer Visio...

    2023

  35. [44]

    Uni-paint: A unified framework for multimodal image inpainting with pretrained diffusion model

    Shiyuan Yang, Xiaodong Chen, and Jing Liao. Uni-paint: A unified framework for multimodal image inpainting with pretrained diffusion model. In Proceedings of the 31st ACM International Conference on Multimedia, pages 3190–3199,

  36. [45]

    Real3d-portrait: One-shot realistic 3d talking portrait synthesis

    Zhenhui Ye, Tianyun Zhong, Yi Ren, Jiaqi Yang, Weichuang Li, Jiawei Huang, Ziyue Jiang, Jinzheng He, Rongjie Huang, Jinglin Liu, et al. Real3d-portrait: One-shot realistic 3d talking portrait synthesis. arXiv preprint arXiv:2401.08503,

    arXiv

  37. [46]

    Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models

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

    2024

  38. [47]

    3d gan inversion with facial sym- metry prior

    Feng Yin, Yifan Zhang, Xiaoming Wang, Ting Wang, Xi- aoyu Li, Yifan Gong, Yuchen Fan, Xiaodong Cun, Ying Shan, and Cengiz Oztireli. 3d gan inversion with facial sym- metry prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 342–351,

  39. [48]

    3d gan inversion with facial symmetry prior

    Fei Yin, Yong Zhang, Xuan Wang, Tengfei Wang, Xiaoyu Li, Yuan Gong, Yanbo Fan, Xiaodong Cun, Ying Shan, Cengiz Oztireli, et al. 3d gan inversion with facial symmetry prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 342–351, 2023. 2

    2023

  40. [49]

    Paint3d: Paint anything 3d with lighting-less texture diffusion models

    Xianfang Zeng, Xin Chen, Zhongqi Qi, Wen Liu, Zibo Zhao, Zhibin Wang, Bin Fu, Yong Liu, and Gang Yu. Paint3d: Paint anything 3d with lighting-less texture diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4252–4262, 2024. 2

    2024

  41. [50]

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

    Bowen Zhang, Yiji Cheng, Chunyu Wang, Ting Zhang, Jiao- long Yang, Yansong Tang, Feng Zhao, Dong Chen, and Bain- ing Guo. Rodinhd: High-fidelity 3d avatar generation with diffusion models. arXiv preprint arXiv:2407.06938, 2024. 2

    2024 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.