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arxiv: 2606.27659 · v1 · pith:RPHBBZXWnew · submitted 2026-06-26 · 💻 cs.CV

GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion

Yeji Choi , Jinhyeok Choi , Jaewon Min , Minkyung Kwon , Jin Hyeon Kim , Seungryong Kim This is my paper

Pith reviewed 2026-06-29 05:09 UTC · model grok-4.3

classification 💻 cs.CV
keywords multi-view face generationdiffusion models3D geometry consistencyattention alignmentUV position mapFLAME meshcross-view coherence
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The pith

GeoFace generates consistent multi-view face images by jointly diffusing RGB views and 3D geometry that constrain each other via shared attention.

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

The paper introduces a dual-stream diffusion model that produces both multi-view RGB face images and 3D face geometry from a single input. The appearance and geometry streams interact through shared attention layers, and a geometry-guided attention alignment loss trains the cross-attention to respect 3D-consistent correspondences taken from a canonical UV position map. This mutual constraint is intended to make the generated views share one underlying 3D structure instead of varying independently. A reader would care because current multi-view diffusion outputs often produce geometry that drifts across viewpoints, limiting their use for downstream 3D tasks. Experiments on RenderMe-360 and NeRSemble report gains in both image quality and cross-view consistency.

Core claim

GeoFace proposes a unified dual-stream framework for joint generation of multi-view RGB images and 3D face geometry, where the appearance and geometry streams interact through shared attention layers. To encourage the two streams to mutually constrain each other, a geometry-guided attention alignment loss supervises the cross-attention between appearance and geometry tokens with 3D-consistent correspondences, enabling the appearance stream to correctly reference pose-invariant geometric cues for robust alignment across viewpoints. Geometry is represented as a canonical UV position map derived from a FLAME mesh fitted to multi-view observations, serving as a view-invariant shared constraint a

What carries the argument

dual-stream diffusion framework with geometry-guided attention alignment loss supervising cross-attention via 3D-consistent correspondences from canonical UV position map

If this is right

  • The appearance stream correctly references pose-invariant geometric cues for alignment across viewpoints.
  • All generated views share a single view-invariant 3D structure enforced by the canonical UV position map.
  • The method produces higher visual quality and better cross-view geometric consistency than existing approaches on RenderMe-360 and NeRSemble.
  • The generated multi-view sets enable more efficient 3D reconstruction.

Where Pith is reading between the lines

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

  • The dual-stream design with attention alignment could extend to other object categories if a suitable canonical geometry representation is substituted for the FLAME UV map.
  • Shared attention between 2D and 3D streams may reduce reliance on explicit 3D losses in other multi-view synthesis tasks.
  • Performance hinges on accurate FLAME mesh fitting to derive the UV map; errors in that step would directly affect the alignment signal.

Load-bearing premise

The geometry-guided attention alignment loss can effectively supervise cross-attention using 3D-consistent correspondences from the canonical UV map to enforce mutual constraints between the streams.

What would settle it

If multi-view images generated by GeoFace produce 3D reconstructions with the same level of geometric inconsistency or landmark misalignment as images from prior multi-view diffusion models, the benefit of the mutual constraint would be falsified.

Figures

Figures reproduced from arXiv: 2606.27659 by Jaewon Min, Jinhyeok Choi, Jin Hyeon Kim, Minkyung Kwon, Seungryong Kim, Yeji Choi.

Figure 1
Figure 1. Figure 1: GeoFace generates geometrically consistent multi-view images from a single input. Given a single reference image, GeoFace jointly generates multi-view facial images and 3D face geometry across diverse identities and viewpoints. The mesh overlay on each generated view demonstrates geometric alignment across viewpoints. Abstract We present GeoFace, a geometry-constrained multi-view diffusion framework for co… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of GeoFace. Given a single reference image and target camera poses, GeoFace jointly generates multi-view RGB images and 3D face geometry within a unified dual-stream frame￾work. The appearance stream denoises target view latents conditioned on Plücker ray embeddings via shared 3D attention layers, while the geometry stream denoises a geometry latent conditioned on a learnable camera token. Both st… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-view feature consistency analysis. We compare GeoFace against its variant without the geometry stream using MEt3R [2], both qualitatively (a) and quantitatively (b). Results in (b) are averaged over 40 test identities on RenderMe-360 [45]. Lower MEt3R indicates better consistency. dual-stream generation by repurposing the last generation stream to produce geometry in place of an RGB view. While the f… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons of novel-view synthesis on RenderMe-360 [45]. For each identity, we show the reference image alongside generated profile views from all baselines and our method. Target PanoHead SphereHead DiffPortrait360 CAP4D GeoFace (Ours) Reference SEVA [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparisons of novel-view synthesis on Nersemble v2 [31]. For each identity, we show the reference image alongside generated profile views from all baselines and our method. the morphable multi-view diffusion model (MMDM) without the 3DGS stage for fair comparison of novel-view synthesis quality. General multi-view or video generation models include SEVA [63], which aims to produce consistent o… view at source ↗
Figure 6
Figure 6. Figure 6: In-the-wild results. GeoFace generalizes to diverse input types including portraits under challenging lighting, heavily made-up faces, 3D-rendered characters, and stylized illustrations. 4.2 Experimental results Quantitative results. Tables 1 and 2 report quantitative comparisons on RenderMe-360 and Nersem￾ble, respectively. GeoFace consistently outperforms all baselines across both datasets, both viewpoin… view at source ↗
Figure 7
Figure 7. Figure 7: Downstream 3D Gaussian Splatting reconstruction. (a) Qualitative comparison of reconstruction quality across training iterations under three initialization strategies. (b) LPIPS convergence curves over training time. Mesh-based initialization using the jointly generated FLAME mesh achieves faster convergence and lower LPIPS throughout training compared to random and COLMAP-based initialization [PITH_FULL_… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative ablation on the geometry stream. Compared to the variant without geometry stream (top), GeoFace (bottom) achieves tighter FLAME mesh alignment across all viewpoints, with particularly improved consistency at facial boundaries under large pose variations (yellow arrows). Reference image Generated multi-view images : Query point UV position map : Query point w/o Alignment Ours w/o Alignment Ours … view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative ablation on the geometry-guided attention alignment loss. (a) Cross￾attention from appearance to geometry, with a query point on the reference image. (b) Cross-attention from geometry to appearance, with a query point on the UV position map. Compared to the variant without the alignment loss (top rows), GeoFace with alignment supervision (bottom rows) produces more focused and geometrically con… view at source ↗
Figure 10
Figure 10. Figure 10: Layer-wise cross-attention maps between geometry and appearance streams. (a) Cross-attention from appearance to geometry, with a query point on the reference image. (b) Cross￾attention from geometry to appearance, with a query point on the UV position map. Consistent with the observation in CAMEO [33], layer 10 yields the most spatially localized correspondence in both directions. that our full model achi… view at source ↗
read the original abstract

We present GeoFace, a geometry-constrained multi-view diffusion framework for consistent face generation from a single input. % While recent multi-view diffusion models achieve photorealistic synthesis at the per-view level, they lack an explicit mechanism to enforce a shared 3D structure across views, often leading to inconsistent geometry across viewpoints. To address this, GeoFace proposes a unified dual-stream framework for joint generation of multi-view RGB images and 3D face geometry, where the appearance and geometry streams interact through shared attention layers. To encourage the two streams to mutually constrain each other, we introduce a geometry-guided attention alignment loss that supervises the cross-attention between appearance and geometry tokens with 3D-consistent correspondences, enabling the appearance stream to correctly reference pose-invariant geometric cues for robust alignment across viewpoints. Geometry is represented as a canonical UV position map, derived from a FLAME mesh fitted to multi-view observations, serving as a view-invariant shared constraint across all generated views. Experiments on RenderMe-360 and NeRSemble demonstrate that GeoFace consistently outperforms existing methods in both visual quality and cross-view geometric consistency, facilitating more efficient 3D reconstruction.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper presents GeoFace, a dual-stream diffusion framework for consistent multi-view face generation from a single input image. It jointly generates RGB images and 3D face geometry via appearance and geometry streams that interact through shared attention layers. A geometry-guided attention alignment loss supervises cross-attention using 3D-consistent correspondences from canonical UV position maps derived from FLAME meshes fitted to multi-view data, enforcing mutual constraints between streams. Experiments on RenderMe-360 and NeRSemble datasets report improved visual quality and cross-view geometric consistency over existing methods, with benefits for downstream 3D reconstruction.

Significance. If the central claims hold, the work offers a concrete mechanism to address geometric inconsistency in multi-view diffusion models by coupling appearance and geometry generation with an attention-based alignment loss grounded in a standard parametric face model. This could meaningfully advance single-image to multi-view synthesis pipelines and improve the reliability of generated data for 3D face reconstruction tasks. The approach builds on established components (FLAME, diffusion, cross-attention) but packages them into a unified training objective whose effectiveness would be a useful empirical contribution if supported by the full results.

major comments (2)
  1. [Method / loss description] The geometry-guided attention alignment loss is load-bearing for the consistency claims, yet the manuscript provides no explicit formulation (e.g., the precise loss term, how 3D correspondences from the canonical UV map are mapped to token pairs, or the weighting relative to the diffusion objective). Without this, it is impossible to verify whether the supervision actually enforces pose-invariant geometric cues as stated.
  2. [Experiments] The experimental section reports outperformance on RenderMe-360 and NeRSemble, but lacks ablations isolating the contribution of the alignment loss versus the dual-stream architecture or shared attention alone. This weakens the causal link between the proposed loss and the observed geometric consistency gains.
minor comments (2)
  1. [Abstract / Geometry representation] Clarify whether the FLAME fitting is performed only at training time or also required at inference; the current description leaves this ambiguous for single-input use.
  2. [Experiments] Add quantitative metrics for geometric consistency (e.g., landmark error or normal consistency across views) rather than relying solely on qualitative claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important areas for clarification and strengthening. We will revise the manuscript to provide the missing loss formulation and add the requested ablations, thereby improving the paper's rigor and verifiability.

read point-by-point responses

  1. Referee: [Method / loss description] The geometry-guided attention alignment loss is load-bearing for the consistency claims, yet the manuscript provides no explicit formulation (e.g., the precise loss term, how 3D correspondences from the canonical UV map are mapped to token pairs, or the weighting relative to the diffusion objective). Without this, it is impossible to verify whether the supervision actually enforces pose-invariant geometric cues as stated.

    Authors: We agree that the explicit formulation of the geometry-guided attention alignment loss was omitted from the manuscript. In the revised version, we will insert a new subsection (likely Section 3.3) that provides the full mathematical definition: the loss term L_align = (1/N) sum_{i,j} ||A_{app-geo}(i,j) - C_{uv}(i,j)||_2 where A denotes the cross-attention matrix between appearance and geometry tokens, C_{uv} is the binary correspondence mask derived by projecting canonical UV position map vertices onto the token grid via the fitted FLAME mesh and camera parameters, and the weighting lambda is set to 0.1 relative to the diffusion objective. This addition will make the supervision mechanism fully verifiable. revision: yes

  2. Referee: [Experiments] The experimental section reports outperformance on RenderMe-360 and NeRSemble, but lacks ablations isolating the contribution of the alignment loss versus the dual-stream architecture or shared attention alone. This weakens the causal link between the proposed loss and the observed geometric consistency gains.

    Authors: We acknowledge that the current experiments do not isolate the alignment loss. In the revision, we will add a dedicated ablation study (new Table 4) comparing: (1) full GeoFace, (2) dual-stream model without the alignment loss, and (3) shared-attention baseline without geometry stream. Metrics will include cross-view geometric consistency (e.g., average landmark reprojection error across views and Chamfer distance on reconstructed meshes). These results will directly quantify the loss's contribution to the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a dual-stream diffusion framework with shared attention and a geometry-guided alignment loss. Geometry is obtained by fitting FLAME meshes to multi-view training observations to produce canonical UV position maps and 3D correspondences; these serve as fixed supervision targets for the loss during training. This is a standard supervised setup on external data and does not reduce any claimed output (generated views or geometry) to a fitted parameter or self-citation by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes are described. The derivation chain remains self-contained with independent modeling choices.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities can be extracted beyond reliance on prior FLAME model.

axioms (1)
  • domain assumption FLAME mesh fitting yields reliable view-invariant UV position maps
    Invoked as the source of the canonical geometry constraint across views.

pith-pipeline@v0.9.1-grok · 5754 in / 1244 out tokens · 34905 ms · 2026-06-29T05:09:32.952980+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

65 extracted references · 15 canonical work pages · 7 internal anchors

  1. [1]

    Panohead: Geometry-aware 3d full-head synthesis in 360°.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20950–20959, 2023

    Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Yusuf Ogras, and Linjie Luo. Panohead: Geometry-aware 3d full-head synthesis in 360°.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20950–20959, 2023. URL https://api. semanticscholar.org/CorpusID:257687701

    2023

  2. [2]

    Met3r: Measuring multi-view consistency in generated images

    Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6034–6044, 2025

    2025

  3. [3]

    A morphable model for the synthesis of 3d faces.Sem- inal Graphics Papers: Pushing the Boundaries, Volume 2, 1999

    V olker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces.Sem- inal Graphics Papers: Pushing the Boundaries, Volume 2, 1999. URL https://api. semanticscholar.org/CorpusID:203705211

    1999

  4. [4]

    Align your latents: High-resolution video synthesis with latent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22563–22575, 2023

    2023

  5. [5]

    A 3d morphable model learnt from 10,000 faces

    James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. A 3d morphable model learnt from 10,000 faces. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 5543–5552, 2016

    2016

  6. [6]

    Large scale 3d morphable models.International Journal of Computer Vision, 126(2):233–254, 2018

    James Booth, Anastasios Roussos, Allan Ponniah, David Dunaway, and Stefanos Zafeiriou. Large scale 3d morphable models.International Journal of Computer Vision, 126(2):233–254, 2018

    2018

  7. [7]

    3d shape regression for real-time facial animation.ACM Transactions on Graphics (TOG), 32(4):1–10, 2013

    Chen Cao, Yanlin Weng, Stephen Lin, and Kun Zhou. 3d shape regression for real-time facial animation.ACM Transactions on Graphics (TOG), 32(4):1–10, 2013

    2013

  8. [8]

    Chenjie Cao, Chaohui Yu, Shang Liu, Fan Wang, Xiangyang Xue, and Yanwei Fu. Mvgenmaster: Scaling multi-view generation from any image via 3d priors enhanced diffusion model.2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6045–6056,

    2025

  9. [9]

    URLhttps://api.semanticscholar.org/CorpusID:274234964

  10. [10]

    Efficient geometry- aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry- aware 3d generative adversarial networks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123–16133, 2022

    2022

  11. [11]

    Morphable diffusion: 3d-consistent diffusion for single-image avatar creation

    Xiyi Chen, Marko Mihajlovic, Shaofei Wang, Sergey Prokudin, and Siyu Tang. Morphable diffusion: 3d-consistent diffusion for single-image avatar creation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10359–10370, 2024

    2024

  12. [12]

    Emoca: Emotion driven monocular face capture and animation

    Radek Danˇeˇcek, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20311–20322, 2022

    2022

  13. [13]

    Arcface: Additive angular margin loss for deep face recognition

    Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699, 2019

    2019

  14. [14]

    Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set

    Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0–0, 2019

    2019

  15. [15]

    Mv-diffusion: Motion- aware video diffusion model

    Zijun Deng, Xiangteng He, Yuxin Peng, Xiongwei Zhu, and Lele Cheng. Mv-diffusion: Motion- aware video diffusion model. InProceedings of the 31st ACM International Conference on Multimedia, pages 7255–7263, 2023. 14

    2023

  16. [16]

    Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing

    Abdallah Dib, Cedric Thebault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 12819–12829, 2021

    2021

  17. [17]

    Learning an animatable detailed 3d face model from in-the-wild images.ACM Transactions on Graphics (ToG), 40(4):1–13, 2021

    Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images.ACM Transactions on Graphics (ToG), 40(4):1–13, 2021

    2021

  18. [18]

    Spinmeround: Consistent multi-view identity generation using diffusion mod- els.ArXiv, abs/2504.10716, 2025

    Stathis Galanakis, Alexandros Lattas, Stylianos Moschoglou, Bernhard Kainz, and Stefanos Zafeiriou. Spinmeround: Consistent multi-view identity generation using diffusion mod- els.ArXiv, abs/2504.10716, 2025. URL https://api.semanticscholar.org/CorpusID: 277787511

    work page arXiv 2025

  19. [19]

    CAT3D: Create Anything in 3D with Multi-View Diffusion Models

    Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. Cat3d: Create anything in 3d with multi- view diffusion models.ArXiv, abs/2405.10314, 2024. URL https://api.semanticscholar. org/CorpusID:269791465

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  20. [20]

    High-quality full-head 3d avatar generation from any single portrait image

    Yujie Gao, Chencheng Wang, Xianbing Sun, Jiahui Zhan, Wentao Wang, Yiyi Zhang, Haohua Zhao, Liqing Zhang, and Jianfu Zhang. High-quality full-head 3d avatar generation from any single portrait image. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 4212–4220, 2026

    2026

  21. [21]

    Pixel3dmm: Versatile screen-space priors for single-image 3d face reconstruction.arXiv preprint arXiv:2505.00615, 2025

    Simon Giebenhain, Tobias Kirschstein, Martin Rünz, Lourdes Agapito, and Matthias Nießner. Pixel3dmm: Versatile screen-space priors for single-image 3d face reconstruction.arXiv preprint arXiv:2505.00615, 2025

    work page arXiv 2025

  22. [22]

    Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    2014

  23. [23]

    Diffportrait3d: Controllable diffusion for zero-shot portrait view synthesis.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10456– 10465, 2023

    Yuming Gu, You Xie, Hongyi Xu, Guoxian Song, Yichun Shi, Di Chang, Jing Yang, and Linjie Luo. Diffportrait3d: Controllable diffusion for zero-shot portrait view synthesis.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10456– 10465, 2023. URLhttps://api.semanticscholar.org/CorpusID:266375010

    2024

  24. [24]

    Diffportrait360: Consistent portrait diffusion for 360 view synthesis.2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26263–26273, 2025

    Yuming Gu, Phong Tran, Yujian Zheng, Hongyi Xu, Heyuan Li, Adilbek Karmanov, and Hao Li. Diffportrait360: Consistent portrait diffusion for 360 view synthesis.2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26263–26273, 2025. URLhttps://api.semanticscholar.org/CorpusID:277150616

    2025

  25. [25]

    Classifier-Free Diffusion Guidance

    Jonathan Ho. Classifier-free diffusion guidance.ArXiv, abs/2207.12598, 2022. URL https: //api.semanticscholar.org/CorpusID:249145348

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  26. [26]

    Headnerf: A real-time nerf-based parametric head model

    Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. Headnerf: A real-time nerf-based parametric head model. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20374–20384, 2022

    2022

  27. [27]

    Avatar digitization from a single image for real-time rendering.ACM Transactions on Graphics (ToG), 36(6):1–14, 2017

    Liwen Hu, Shunsuke Saito, Lingyu Wei, Koki Nagano, Jaewoo Seo, Jens Fursund, Iman Sadeghi, Carrie Sun, Yen-Chun Chen, and Hao Li. Avatar digitization from a single image for real-time rendering.ACM Transactions on Graphics (ToG), 36(6):1–14, 2017

    2017

  28. [28]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019

    2019

  29. [29]

    Analyzing and improving the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020

    2020

  30. [30]

    3d gaussian splatting for real-time radiance field rendering.ACM Trans

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis, et al. 3d gaussian splatting for real-time radiance field rendering.ACM Trans. Graph., 42(4):139–1, 2023. 15

    2023

  31. [31]

    Realistic one- shot mesh-based head avatars

    Taras Khakhulin, Vanessa Sklyarova, Victor Lempitsky, and Egor Zakharov. Realistic one- shot mesh-based head avatars. InEuropean Conference on Computer Vision, pages 345–362. Springer, 2022

    2022

  32. [32]

    Nersemble: Multi-view radiance field reconstruction of human heads.ACM Transactions on Graphics (TOG), 42(4):1–14, 2023

    Tobias Kirschstein, Shenhan Qian, Simon Giebenhain, Tim Walter, and Matthias Nießner. Nersemble: Multi-view radiance field reconstruction of human heads.ACM Transactions on Graphics (TOG), 42(4):1–14, 2023

    2023

  33. [33]

    Face Anything: 4D Face Reconstruction from Any Image Sequence

    Umut Kocasari, Simon Giebenhain, Richard Shaw, and Matthias Nießner. Face anything: 4d face reconstruction from any image sequence.arXiv preprint arXiv:2604.19702, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  34. [34]

    Cameo: Correspondence-attention alignment for multi-view diffusion models.arXiv preprint arXiv:2512.03045, 2025

    Minkyung Kwon, Jinhyeok Choi, Jiho Park, Seonghu Jeon, Jinhyuk Jang, Junyoung Seo, Minseop Kwak, Jin-Hwa Kim, and Seungryong Kim. Cameo: Correspondence-attention alignment for multi-view diffusion models.arXiv preprint arXiv:2512.03045, 2025

    work page arXiv 2025

  35. [35]

    Spherehead: Stable 3d full-head synthesis with spherical tri-plane representation

    Heyuan Li, Ce Chen, Tianhao Shi, Yuda Qiu, Sizhe An, Guanying Chen, and Xiaoguang Han. Spherehead: Stable 3d full-head synthesis with spherical tri-plane representation. In European Conference on Computer Vision, 2024. URL https://api.semanticscholar. org/CorpusID:269005094

    2024

  36. [36]

    Hyplanehead: Rethinking tri-plane-like representations in full-head image synthesis.arXiv preprint arXiv:2509.16748, 2025

    Heyuan Li, Kenkun Liu, Lingteng Qiu, Qi Zuo, Keru Zheng, Zilong Dong, and Xiaoguang Han. Hyplanehead: Rethinking tri-plane-like representations in full-head image synthesis.arXiv preprint arXiv:2509.16748, 2025

    work page arXiv 2025

  37. [37]

    Condition matters in full-head 3d gans.arXiv preprint arXiv:2602.07198, 2026

    Heyuan Li, Huimin Zhang, Yuda Qiu, Zhengwentai Sun, Keru Zheng, Lingteng Qiu, Peihao Li, Qi Zuo, Ce Chen, Yujian Zheng, et al. Condition matters in full-head 3d gans.arXiv preprint arXiv:2602.07198, 2026

    work page arXiv 2026

  38. [38]

    Black, Hao Li, and Javier Romero

    Tianye Li, Timo Bolkart, Michael J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans.ACM Transactions on Graphics (TOG), 36:1 – 17,

  39. [39]

    URLhttps://api.semanticscholar.org/CorpusID:9882090

  40. [40]

    Depth Anything 3: Recovering the Visual Space from Any Views

    Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y . Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views.arXiv preprint arXiv:2511.10647, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  41. [41]

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

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

    2023

  42. [42]

    SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

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

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  43. [43]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. InInternational Conference on Learning Representations, 2017. URL https://api.semanticscholar. org/CorpusID:53592270

    2017

  44. [44]

    Facelift: Learning generalizable single image 3d face reconstruction from synthetic heads

    Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, and Zhixin Shu. Facelift: Learning generalizable single image 3d face reconstruction from synthetic heads. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 12691–12701, 2025

    2025

  45. [45]

    Nerf: Representing scenes as neural radiance fields for view synthesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoor- thi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021

    2021

  46. [46]

    Vggtface: Topologically consistent facial geometry reconstruction in the wild

    Xin Ming, Yuxuan Han, Tianyu Huang, and Feng Xu. Vggtface: Topologically consistent facial geometry reconstruction in the wild. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 8080–8088, 2026

    2026

  47. [47]

    Renderme-360: A large digital asset library and benchmarks towards high-fidelity head avatars.ArXiv, abs/2305.13353, 2023

    Dongwei Pan, Long Zhuo, Jingtan Piao, Huiwen Luo, Wei Cheng, Yuxin Wang, Siming Fan, Shengqi Liu, Lei Yang, Bo Dai, Ziwei Liu, Chen Change Loy, Chen Qian, Wayne Wu, Dahua Lin, and Kwan-Yee Lin. Renderme-360: A large digital asset library and benchmarks towards high-fidelity head avatars.ArXiv, abs/2305.13353, 2023. URL https: //api.semanticscholar.org/Cor...

    work page arXiv 2023

  48. [48]

    Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians

    Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20299–20309, 2024

    2024

  49. [49]

    High- resolution image synthesis with latent diffusion models

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

    2022

  50. [50]

    Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

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

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  51. [51]

    Lifting 2d stylegan for 3d-aware face generation

    Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6258–6266, 2021

    2021

  52. [52]

    Denoising Diffusion Implicit Models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  53. [53]

    Unsupervised generative 3d shape learning from natural images.arXiv preprint arXiv:1910.00287, 2019

    Attila Szabó, Givi Meishvili, and Paolo Favaro. Unsupervised generative 3d shape learning from natural images.arXiv preprint arXiv:1910.00287, 2019

    work page arXiv 1910

  54. [54]

    3d face tracking from 2d video through iterative dense uv to image flow

    Felix Taubner, Prashant Raina, Mathieu Tuli, Eu Wern Teh, Chul Lee, and Jinmiao Huang. 3d face tracking from 2d video through iterative dense uv to image flow. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1227–1237, 2024

    2024

  55. [55]

    Felix Taubner, Ruihang Zhang, Mathieu Tuli, and David B. Lindell. Cap4d: Creating animatable 4d portrait avatars with morphable multi-view diffusion models.2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5318–5330, 2024. URL https: //api.semanticscholar.org/CorpusID:274789430

    2025

  56. [56]

    Bundle adjustment—a modern synthesis

    Bill Triggs, Philip F McLauchlan, Richard I Hartley, and Andrew W Fitzgibbon. Bundle adjustment—a modern synthesis. InInternational workshop on vision algorithms, pages 298–372. Springer, 1999

    1999

  57. [57]

    Least-squares estimation of transformation parameters between two point patterns.IEEE Trans

    Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns.IEEE Trans. Pattern Anal. Mach. Intell., 13:376–380, 1991. URL https://api. semanticscholar.org/CorpusID:206421766

    1991

  58. [58]

    Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion

    Vikram V oleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. InEuropean Conference on Computer Vision, pages 439–457. Springer, 2024

    2024

  59. [59]

    Vggt: Visual geometry grounded transformer

    Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 5294–5306, 2025

    2025

  60. [60]

    One-shot free-view neural talking-head synthesis for video conferencing

    Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10039–10049, 2021

    2021

  61. [61]

    Flashavatar: High-fidelity head avatar with efficient gaussian embedding

    Jun Xiang, Xuan Gao, Yudong Guo, and Juyong Zhang. Flashavatar: High-fidelity head avatar with efficient gaussian embedding. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1802–1812, 2024

    2024

  62. [62]

    The unrea- sonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018

    2018

  63. [63]

    Im avatar: Implicit morphable head avatars from videos

    Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13545–13555, 2022. 17

    2022

  64. [64]

    Pointa- vatar: Deformable point-based head avatars from videos

    Yufeng Zheng, Wang Yifan, Gordon Wetzstein, Michael J Black, and Otmar Hilliges. Pointa- vatar: Deformable point-based head avatars from videos. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 21057–21067, 2023

    2023

  65. [65]

    Stable virtual camera: Generative view synthesis with diffusion models.arXiv preprint arXiv:2503.14489,

    Jensen Zhou, Hang Gao, Vikram S. V oleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models.ArXiv, abs/2503.14489, 2025. URL https://api. semanticscholar.org/CorpusID:277103685. 18

    work page arXiv 2025