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REVIEW 3 major objections 6 minor 102 references

Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper proposes Arc2Avatar, a pipeline that turns a single facial photograph into a complete, animatable 3D head by distilling a face foundation model into 3D Gaussian splats.

desk verdict A well-engineered single-image avatar pipeline with a genuinely new combination, but the headline identity claim leans on an evaluation loop that needs closing. read the letter →

arxiv 2501.05379 v2 pith:A2IB5BMM submitted 2025-01-09 cs.CV

Dimitrios Gerogiannis , Foivos Paraperas Papantoniou , Rolandos Alexandros Potamias , Alexandros Lattas , Stefanos Zafeiriou This is my paper · ORCID
classification cs.CV
keywords 3DavatargenerationscoredistillationsamplingGaussiansplattingidentitypreservationfacefoundationmodelblendshapeexpressionsingle-imagereconstructionFLAMEtemplate
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

Arc2Avatar claims that one facial photograph is enough to generate a full 3D head avatar with high realism and strong identity preservation, using a face-specific diffusion model as the guidance for score distillation. The key move is to adapt the Arc2Face foundation model, which normally generates only frontal faces, into a 360-degree head prior by fine-tuning it on synthetic PanoHead renderings. This prior is then used to optimize 3D Gaussian splats that stay attached to a FLAME mesh template, so the resulting avatar can be reanimated with blendshapes. The paper reports lower FID than competing methods, the highest identity similarity scores, and 93 percent user preference in its comparisons.

What carries the argument

The load-bearing mechanism is score distillation using Interval Score Matching (ISM), with the adapted Arc2Face model as the denoiser. Arc2Face is a Stable Diffusion variant conditioned on an ArcFace identity embedding rather than text; the paper fine-tunes it with LoRA on synthetic PanoHead renderings so it can denoise side and back views, then blends its identity-conditioned embedding with CLIP view embeddings at a 0.85-to-0.15 ratio. The 3D side is a masked 3D Gaussian Splatting optimization on splats initialized from a densely upsampled FLAME mesh, with L2 position and Laplacian regularization keeping facial splats attached to the template while non-facial splats retain standard densification for hair and ears. The ISM formulation with DDIM inversion supplies consistent pseudo-ground-truth views, which is what lets the method run at a low guidance scale and avoid the color oversaturation typical of SDS.

What would settle it

Render back and side views of avatars generated from a set of subjects with distinctive hair and ear geometry, then run an identity-matching test, either human or ArcFace-based, comparing those views against the full set of input identities; if the non-frontal views match the wrong identity at chance level, or if ear artifacts appear consistently across subjects, the central claim that the prior preserves identity around 360 degrees fails.

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Extended reading notes

Core claim

The central discovery is that an identity-conditioned face foundation model can be converted into a full-head diffusion prior with a modest amount of synthetic data, and that this prior is strong enough to drive SDS/ISM optimization of Gaussian splats from a single ArcFace embedding. The identity embedding replaces text as the main conditioning signal, while weak CLIP-based view embeddings are blended in to distinguish front, side, and back views. Because the splats are initialized one-to-one on a densely subdivided FLAME mesh and protected from densification and pruning in the facial area, while being pulled toward the template by L2 and Laplacian regularizers, the final avatar remains riggable and expressions can be applied through blendshape deformations. The authors present this as the first SDS-based method that reaches state-of-the-art realism and identity fidelity for single-image 3D avatar generation by using a human face foundation model as guidance.

Load-bearing premise

The method assumes that the LoRA-adapted Arc2Face model, trained on synthetic PanoHead renderings, gives identity-consistent and artifact-free denoising guidance for back and side views when the only identity signal is a frontal photo.

Editorial extensions

If this is right

  • If the method works as reported, a single phone-style photo is enough to produce a full 3D head that can be rendered from any angle and reanimated with standard facial blendshapes.
  • Because the identity embedding can be averaged over several photos, the same pipeline improves with more input, but does not require it.
  • The optional SDS refinement step, run for about 500 iterations after applying an extreme blendshape, fills in mouth interior details such as teeth and tongue, so open-mouth expressions remain realistic.
  • The low guidance scale enabled by the strong identity prior means the method avoids the oversaturated colors that plague text-driven SDS avatars without sacrificing geometric detail.
  • The dense FLAME correspondence makes the avatars compatible with existing blendshape-driven animation pipelines, not just with the paper's own renderer.

Reading between the lines

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

  • The same recipe—fine-tune a domain-specific diffusion model on synthetic multi-view renderings and use its identity embedding as the SDS conditioner—could transfer to other categories with a strong parametric template, such as hands or full bodies, where the template supplies riggability and the diffusion model supplies appearance.
  • Whether the back and side views are truly identity-consistent or merely plausible is not fully settled by frontal-image ArcFace similarity; a direct test would render avatars from subjects with distinctive hair or ears and ask humans to match side and back views to the correct identity.
  • The reliance on PanoHead synthetic data suggests a ceiling: artifacts in the generated training set, such as ear and long-hair errors, are inherited by the LoRA-adapted prior, so a better synthetic 360-degree head generator should directly reduce artifacts in the avatars.
  • Blendshape editing combined with SDS refinement hints at a cheap way to edit avatars post-hoc: apply any mesh deformation and let a few hundred distillation steps re-synthesize plausible detail, which could be generalized to expressions, aging, or hairstyle changes.
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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

3 major / 6 minor

Summary. The paper proposes Arc2Avatar, a score-distillation-based method for generating a full 3D head avatar from a facial image, built on 3D Gaussian Splatting anchored to a FLAME template. It fine-tunes Arc2Face with LoRA on synthetic PanoHead renderings to enable side- and back-view generation, blends the ArcFace identity embedding with CLIP view embeddings inside an ISM distillation loop, and uses masked 3DGS plus L2 and Laplacian regularizers to preserve dense mesh correspondence and support blendshape-driven expressions. The experiments report Identity Similarity Distribution, FID, qualitative comparisons, and a user study against ID-to-3D, Magic123, DreamCraft3D, TADA, DreamFace, Fantasia3D, and HumanNorm.

Significance. If the identity-preservation claim is substantiated, this is a meaningful step toward single-image, animatable, ID-consistent 3D avatars: it combines a face-specific diffusion prior with 3DGS and a 3DMM in a way that previous SDS-based methods did not. The paper is strong on technical detail: the optimization schedule, hyperparameters, masking strategy, and supplementary ablations are described carefully, and the qualitative results are plausible and visually compelling. However, the quantitative support for the central identity claim is partly circular and the experiments do not evaluate the single-image regime stated in the title and abstract. The realism evidence from FID and the user study is useful, but it does not independently validate identity preservation. For these reasons the contribution is promising but provisional.

major comments (3)
  1. [Sec. 3.3 / Sec. 4.2] The identity-preservation metric is circular with respect to the central claim. In Sec. 3.3, the generator is conditioned on the ArcFace embedding v = alpha(x) through the identity-conditioned text embedding c_default and the blended condition c_d, and the LoRA-fine-tuned Arc2Face prior is trained to denoise toward images whose ArcFace embedding matches this conditioning. In Sec. 4.2, the same ArcFace network is used to compute cosine similarity between rendered views and real images. A high ISD therefore partly certifies that the 2D diffusion prior reproduces its own conditioning signal rather than that the 3D Gaussian representation independently preserves identity. This is load-bearing because identity preservation is the main quantitative advantage claimed over ID-to-3D. Please re-evaluate with an independent face-recognition embedding family and/or a geometry- or landmark-based identity metric, and report per-view scores for frontal, side, and back renderings.
  2. [Sec. 4 (protocol) vs. Abstract/Title] The headline single-image claim is not evaluated. The experimental protocol states that for each celebrity the authors used 20 images for evaluation and 5 images to compute an average identity embedding for conditioning, following ID-to-3D. The parenthetical that both methods 'can also work with a single image' does not replace an experiment. Since the abstract and title claim generation from a single image, please add a single-image evaluation using one embedding from one input image, and ideally a comparison showing how identity and realism scores vary with the number of conditioning images. This is needed to substantiate a contribution that is currently only demonstrated in the multi-image setting.
  3. [Sec. 3.2 / Sec. 4.2] The 360-degree identity-consistency claim is asserted but not quantitatively measured per view. The LoRA fine-tuning on PanoHead renderings is the main mechanism for side- and back-view guidance, yet the paper also admits PanoHead-related ear artifacts and domain gaps, and the supplementary material shows loss of neutral correspondence. The ISD results in Fig. 5 aggregate over rendered views, and the back views are not separately reported. Please report identity similarity separately for frontal, side, and back renderings, and state how many renders of each type were used. Without this, the claim that Arc2Avatar preserves identity across the full head, not just the frontal region, is only qualitative.
minor comments (6)
  1. [Sec. 2.2] The phrase 'Score Distillation Sampling [65]' cites DreamBooth; the correct citation for SDS is Poole et al. [59].
  2. [Sec. 3.3, Eq. (6)] It is unclear whether the blending in Eq. (6) is performed in the Arc2Face fine-tuned text-encoder space, the original Stable Diffusion text-encoder space, or a shared space; please clarify why linear interpolation between c_default and c_view is well-defined given that they come from two different encoders.
  3. [Supp. A.2 / Supp. E.3.2] Field-of-view values such as 0.4, 0.53, and 0.3 are given without units; please specify whether these are radians, normalized focal lengths, or another convention.
  4. [Sec. 4.3 / Fig. 7] The expression-identity-similarity plot reports only the proposed method; since the text says the protocol follows ID-to-3D, please include the ID-to-3D baseline in the same plot for direct comparison.
  5. [Table 1] The FID comparison uses a 30-celebrity evaluation set, which is small for FID; please report the number of renderings used and ideally confidence intervals or an alternative realism metric.
  6. [Supp. B] The ablations for LoRA fine-tuning and view-embedding weights are presented only as qualitative stills from the first half of optimization; adding quantitative identity-similarity and view-consistency numbers would make the design choices reproducible and verifiable.

Circularity Check

1 steps flagged · score 5.0 of 10

Identity-preservation metric overlaps with the ArcFace conditioning signal, making the absolute ISD claim partly circular; the 3D pipeline itself remains independent.

  1. fitted input called prediction [Sec. 3.3 (Eq. 4) and Sec. 4.2 (ISD evaluation)]
    "Regarding identity, we use the original Arc2Face conditioning, which involves an ArcFace embedding of the input subject. ... Following that, we replace id with v, resulting in an identity-conditioned text embedding: cdefault = Eτ ("photo of a id person", v), (4). ... Using ArcFace [15], we extracted identity embeddings from both the rendered images and the evaluation set for each celebrity. We then calculated the cosine similarity between the rendered embeddings and the real image embeddings to measure identity similarity."

    The conditioning input to the whole pipeline is the ArcFace embedding v = α(x) (Eq. 4), and the headline identity metric is the cosine similarity between α(rendered view) and α(x). Arc2Face is trained to output images whose ArcFace embedding matches the conditioning v, and the Sec. 3.2 LoRA fine-tuning conditions every PanoHead view on the same frontal v. Therefore high ISD on rendered views partly reports that the SDS/ISM optimization stayed within the Arc2Face conditional distribution, not that the 3D representation independently recovered identity.

full rationale

The main circularity is localized to the identity-preservation evaluation: Sec. 3.3 conditions generation on v = α(x) via Arc2Face, and Sec. 4.2 scores with cosine(α(render), α(x)). Since Arc2Face is trained to be ArcFace-consistent, the ISD score partly certifies the prior's conditioning fidelity rather than an independent 3D identity reconstruction. I do not find a load-bearing self-citation chain: Arc2Face [54] is a published external model with its own training and benchmarks, and the 3DGS/FLAME/SDS contributions are not derived from it by definition. The title and abstract claim 'a single image', but Sec. 4 conditions on an average of 5 embeddings per celebrity and never evaluates the single-image regime; this is a missing test rather than a circular step. The paper also acknowledges ear artifacts and occasional loss of neutral correspondence in Sec. 5, which are honest limitations and do not create circularity. Overall the derivation is not equivalent to its inputs by construction, but the central identity claim is weakened by the metric/conditioning overlap, giving a moderate score.

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

The method's contributions rest on a set of hand-tuned hyperparameters (LoRA rank/scale, embedding blend weight, guidance scale, regularizer weights) and on the validity of external priors (Arc2Face, PanoHead, ISM, FLAME). No new conceptual entities are introduced; the LoRA-extended Arc2Face and the curated PanoHead dataset are trained artifacts, not new physical or mathematical objects.

free parameters (5)
  • LoRA rank r = 16
    Chosen after experimentation for a balance between novel-view adaptability and preserving Arc2Face's face quality (Sec. 3.2).
  • LoRA scale s_L = 0.45
    Selected through extensive experimentation to enable side and back views without degrading identity (Sec. 3.3).
  • ID/view blending weight b = 0.85
    Ablations (Suppl. B.2) show lower values reduce identity fidelity and higher values cause Janus artifacts; 0.85 was the manual optimum.
  • Diffusion guidance scale = 1
    A low guidance scale avoids SDS oversaturation, enabled by the strong identity prior; no higher-scale baseline is reported (Suppl. E.3.1).
  • Template regularizer weight (L2 and Laplacian) = 1e+8 each
    High weights chosen empirically to force strong template correspondence during SDS optimization (Suppl. E.6).
assumptions (5)
  • domain assumption Arc2Face provides a valid identity-consistent facial prior that transfers to 3D via SDS.
    The whole ID guidance depends on Arc2Face generating ID-consistent faces from an ArcFace embedding (Sec. 3.1.2, 3.3).
  • domain assumption PanoHead synthetic renderings are realistic and diverse enough to fine-tune Arc2Face for 360-degree generation.
    The LoRA training set is generated solely by PanoHead; the paper acknowledges domain gaps and ear artifacts (Sec. 3.2, Sec. 5).
  • standard math Interval Score Matching yields consistent pseudo-ground-truth views for distillation.
    The method adopts ISM from LucidDreamer [43] as its loss; validity of ISM is assumed from prior publication (Sec. 3.1.1).
  • domain assumption FLAME blendshapes deform the upsampled splat positions consistently.
    Expression generation assumes vertex displacements from FLAME blendshapes transfer through midpoint subdivision to the densified splat cloud (Sec. 3.4, 3.5).
  • standard math The Laplacian regularizer preserves local mesh geometry in the splat positions.
    Inspired by [53], the regularizer minimizes differences between graph Laplacians of splat and template positions (Sec. 3.4).

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Cite this review

Pith. "Pith review of Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance." pith.science (2026). https://pith.science/paper/A2IB5BMM

@misc{pith2026250105379,
  author       = {Pith},
  title        = {Pith review of: Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A2IB5BMM}},
  note         = {Machine review of arXiv:2501.05379}
}
read the original abstract

Inspired by the effectiveness of 3D Gaussian Splatting (3DGS) in reconstructing detailed 3D scenes within multi-view setups and the emergence of large 2D human foundation models, we introduce Arc2Avatar, the first SDS-based method utilizing a human face foundation model as guidance with just a single image as input. To achieve that, we extend such a model for diverse-view human head generation by fine-tuning on synthetic data and modifying its conditioning. Our avatars maintain a dense correspondence with a human face mesh template, allowing blendshape-based expression generation. This is achieved through a modified 3DGS approach, connectivity regularizers, and a strategic initialization tailored for our task. Additionally, we propose an optional efficient SDS-based correction step to refine the blendshape expressions, enhancing realism and diversity. Experiments demonstrate that Arc2Avatar achieves state-of-the-art realism and identity preservation, effectively addressing color issues by allowing the use of very low guidance, enabled by our strong identity prior and initialization strategy, without compromising detail. Please visit https://arc2avatar.github.io for more resources.

Figures

Figures reproduced from arXiv: 2501.05379 by the authors.

Figure 1
Figure 1. Arc2Avatar creates detailed 3D head avatars from a single image with unprecedented realism and identity similarity through a [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the proposed 3D generation framework. Our method uses an adapted Arc2Face diffusion model [54], augmented for diverse view generation through fine-tuning on PanoHead [2] samples. For 3D generation, starting with a frontal image, we extract the Arc2Face embedding and initialize Gaussian Splats on each vertex of the FLAME head model [40], fitting the facial area to the mean facial texture. We then apply an… view at source ↗
Figure 3
Figure 3. LoRA [30] fine-tuning of Arc2Face [54] with PanoHead [2] samples. The generation is conditioned on the frontal sample. 3.3. ID-guided 3D Head Generation using Arc2Face Our distillation method builds upon the work of [43], which optimizes a 3D scene using ISM as an alternative to SDS. We extend this framework to reconstruct 3D head avatars from ID features using our view-augmented face model de￾scribed above for guid… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of competing methods for celebrities. ID-methods use an average ID-embedding from multiple images, although they can also use that of a single image. to compute an average identity embedding to condition the generation, although both our method a…
Figure 5
Figure 5. Figure 5: Identity similarity dis￾tribution for quantitative compar￾ison. FID TADA [44] 213.39 DreamFace [92] 214.58 Fantasia3D [11] 280.32 HumanNorm [31] 173.02 ID-to-3D [4] 154.51 Magic123 [60] 159.21 DreamCraft3D [74] 186.98 Ours 144.58 [PITH_FULL_IMAGE:figures/full_fig_p008…
Figure 7
Figure 7. Figure 7: Identity Similarity Distribution between expressive and neutral render￾ings per celebrity. Firstly, although our initialization strategy and regulariz￾ers typically enforce a neutral pose, there are occasional instances where this neutrality is lost due to the stochast…
Figure 9
Figure 9. Figure 9: , we illustrate the progression of this initial optimiza￾tion, showing how the mean texture-fitted splat smoothly transforms into the subject’s face while maintaining corre￾spondence with the underlying template mesh, highlighting the effectiveness of our approach. B. …
Figure 8
Figure 8. Figure 8: Initial mean texture fitting. The splats in the facial area are optimized based on mesh renderings with the mean texture. In the end, the splats closely replicate the mean textured mesh. A.2. Personalization of the Facial Area As described in the paper, our person-spec…
Figure 10
Figure 10. Figure 10: Mean texture initialization impact. Although tem￾plate regularization achieves geometrical correspondence, the ab￾sence of the proposed initialization (top) leads to significant tex￾ture misalignment, disrupting the overall correspondence. Without mean texture initial…
Figure 11
Figure 11. Figure 11: Impact of augmenting Arc2Face for 360° generation and using view embeddings during distillation. As expected, the default model is limited to modeling the frontal view, resulting in multiple inconsistencies and Janus artifacts in other views, as well as oversaturated …
Figure 12
Figure 12. Figure 12: Arc2Avatar is not limited to celebrities. Our method exhibits strong generalization, providing realistic and consistent 3D avatars for individuals of different ages, ethnicities, and backgrounds. optimization stage despite our efforts to enforce consistency with the n…
Figure 13
Figure 13. Figure 13: Renderings of generated 3D avatars from diverse viewpoints. Our method extends beyond realistic frontal views to produce complete 3D head models that can be rendered from any angle. consistent upsampling within the facial region, we separate the mesh into facial and h…
Figure 14
Figure 14. Figure 14: Failure cases. Artifacts may appear around the ears and neck regions. Additionally, certain inputs can bias the op￾timization towards smiling or surprised expressions, despite the underlying neutral mesh. Nevertheless, the avatars consistently preserve the individuals…

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Reference graph

Works this paper leans on

102 extracted references · 49 canonical work pages

  1. [1]

    https://trimesh.org Version 4.3.1

    Trimesh. https://trimesh.org Version 4.3.1. 15

  2. [2]

    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 (CVPR), pages 20950–20959, 2023. 3, 4, 5, 8

    2023

  3. [3]

    Re- imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond

    Mohammadreza Armandpour, Huangjie Zheng, Ali Sadeghian, Amir Sadeghian, and Mingyuan Zhou. Re- imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond. arXiv preprint arXiv:2304.04968, 2023. 17

    arXiv 2023

  4. [4]

    Id-to-3d: Expressive id-guided 3d heads via score distillation sampling, 2024

    Francesca Babiloni, Alexandros Lattas, Jiankang Deng, and Stefanos Zafeiriou. Id-to-3d: Expressive id-guided 3d heads via score distillation sampling, 2024. 3, 6, 7, 8

    2024

  5. [5]

    A morphable model for the synthesis of 3d faces

    V olker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. Seminal Graphics Papers: Pushing the Boundaries, Volume 2, 1999. 1

    1999

  6. [6]

    Face recognition based on fitting a 3d morphable model

    V olker Blanz and Thomas Vetter. Face recognition based on fitting a 3d morphable model. IEEE Transactions on pattern analysis and machine intelligence , 25(9):1063–1074, 2003. 1, 3

    2003

  7. [7]

    Preface: A data-driven volumetric prior for few-shot ultra high-resolution face synthesis

    Marcel C B ¨uhler, Kripasindhu Sarkar, Tanmay Shah, Gengyan Li, Daoye Wang, Leonhard Helminger, Ser- gio Orts-Escolano, Dmitry Lagun, Otmar Hilliges, Thabo Beeler, et al. Preface: A data-driven volumetric prior for few-shot ultra high-resolution face synthesis. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 3402–3413, 2023. 3

    2023

  8. [8]

    Yukang Cao, Yan-Pei Cao, Kai Han, Ying Shan, and Kwan- Yee K. Wong. Dreamavatar: Text-and-shape guided 3d hu- man avatar generation via diffusion models, 2023. 2

    2023

Show all 102 references
  1. [9]

    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. In Pro- ceedings of the IEEE/CVF conference on computer v...

    2022

  2. [10]

    Photoverse: Tuning-free image cus- tomization with text-to-image diffusion models, 2023

    Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, et al. Photoverse: Tuning-free image cus- tomization with text-to-image diffusion models, 2023. 2

    2023

  3. [11]

    Fan- tasia3d: Disentangling geometry and appearance for high- quality text-to-3d content creation

    Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fan- tasia3d: Disentangling geometry and appearance for high- quality text-to-3d content creation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 22246–22256, 2023. 2, 6, 7, 8

    2023

  4. [12]

    Id-aligner: Enhancing identity- preserving text-to-image generation with reward feedback learning, 2024

    Weifeng Chen, Jiacheng Zhang, Jie Wu, Hefeng Wu, Xue- feng Xiao, and Liang Lin. Id-aligner: Enhancing identity- preserving text-to-image generation with reward feedback learning, 2024. 3

    2024

  5. [13]

    Dreamiden- tity: Improved editability for efficient face-identity preserved image generation

    Zhuowei Chen, Shancheng Fang, Wei Liu, Qian He, Mengqi Huang, Yongdong Zhang, and Zhendong Mao. Dreamiden- tity: Improved editability for efficient face-identity preserved image generation. arXiv preprint arXiv:2307.00300, 2023. 2

    2023 arXiv

  6. [14]

    Idadapter: Learn- ing mixed features for tuning-free personalization of text-to- image models, 2024

    Siying Cui, Jia Guo, Xiang An, Jiankang Deng, Yongle Zhao, Xinyu Wei, and Ziyong Feng. Idadapter: Learn- ing mixed features for tuning-free personalization of text-to- image models, 2024. 3

    2024

  7. [15]

    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. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 4690–4699, 2019. 4, 7

    2019

  8. [16]

    Mosar: Monocular semi-supervised model for avatar reconstruction using differentiable shading

    Abdallah Dib, Luiz Gustavo Hafemann, Emeline Got, Trevor Anderson, Amin Fadaeinejad, Rafael MO Cruz, and Marc- Andr´e Carbonneau. Mosar: Monocular semi-supervised model for avatar reconstruction using differentiable shading. In Proceedings of the IEEE/CVF Conference on Compute...

    2024

  9. [17]

    3d morphable face models—past, present, and future

    Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (ToG), 39(5):1–38, 2020. 3

    2020

  10. [18]

    Dynamic neural radiance fields for monocular 4d facial avatar reconstruction, 2020

    Guy Gafni, Justus Thies, Michael Zollh ¨ofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction, 2020. 1

    2020

  11. [19]

    Dynamic neural radiance fields for monocular 4d facial avatar reconstruction

    Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8649–8658, 2021. 3

    2021

  12. [20]

    An image is worth one word: Personalizing text-to- image generation using textual inversion

    Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patash- nik, Amit H Bermano, Gal Chechik, and Daniel Cohen- Or. An image is worth one word: Personalizing text-to- image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2

    2022 arXiv

  13. [21]

    Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023

    Rinon Gal, Moab Arar, Yuval Atzmon, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023. 2 9

    2023

  14. [22]

    Srinivasan, Jonathan T

    Ruiqi Gao*, Aleksander Holynski*, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole*. Cat3d: Create any- thing in 3d with multi-view diffusion models. Advances in Neural Information Processing Systems, 2024. 3

    2024

  15. [23]

    Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction

    Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1155–1164, 2019. 3

    2019

  16. [24]

    Synthesizing coupled 3d face modalities by trunk-branch generative adversarial networks

    Baris Gecer, Alexandros Lattas, Stylianos Ploumpis, Jiankang Deng, Athanasios Papaioannou, Stylianos Moschoglou, and Stefanos Zafeiriou. Synthesizing coupled 3d face modalities by trunk-branch generative adversarial networks. In European conference on computer vision , pages 4...

    2020

  17. [25]

    Xiao Han, Yukang Cao, Kai Han, Xiatian Zhu, Jiankang Deng, Yi-Zhe Song, Tao Xiang, and Kwan-Yee K. Wong. Headsculpt: Crafting 3d head avatars with text, 2023. 2

    2023

  18. [26]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 8

    2017

  19. [27]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. In Advances in Neural Informa- tion Processing Systems, pages 6840–6851, 2020. 3

    2020

  20. [28]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2

    2020

  21. [29]

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

    Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juy- ong Zhang. Headnerf: A real-time nerf-based parametric head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20374– 20384, 2022. 3

    2022

  22. [30]

    LoRA: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. InIn- ternational Conference on Learning Representations , 2022. 2, 5

    2022

  23. [31]

    Humannorm: Learning normal diffusion model for high-quality and realistic 3d hu- man generation

    Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, and Qing Wang. Humannorm: Learning normal diffusion model for high-quality and realistic 3d hu- man generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024....

    2024

  24. [32]

    Humanrf: High-fidelity neural radiance fields for humans in motion

    Mustafa Is ¸ık, Martin R ¨unz, Markos Georgopoulos, Taras Khakhulin, Jonathan Starck, Lourdes Agapito, and Matthias Nießner. Humanrf: High-fidelity neural radiance fields for humans in motion. ACM Transactions on Graphics, 42(4): 1–12, 2023. 1

    2023

  25. [33]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 3

    2023

  26. [34]

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

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

    2023

  27. [35]

    Multi-concept customization of text-to-image diffusion

    Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1931–1941, 2023. 2

    1931

  28. [36]

    Avatarme++: Facial shape and brdf inference with photorealistic rendering-aware gans

    Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Abhijeet Ghosh, and Stefanos Zafeiriou. Avatarme++: Facial shape and brdf inference with photorealistic rendering-aware gans. IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 44(12): 926...

    2021

  29. [37]

    Fitme: Deep photorealistic 3d morphable model avatars

    Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Jiankang Deng, and Stefanos Zafeiriou. Fitme: Deep photorealistic 3d morphable model avatars. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition , pages 8629–8640, 2023. 3

    2023

  30. [38]

    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,

  31. [39]

    Learning forma- tion of physically-based face attributes

    Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, et al. Learning forma- tion of physically-based face attributes. In Proceedings of the IEEE/CVF conference on computer vision and pattern r...

    2020

  32. [40]

    Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and ex- pression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1–194:17, 2017. 1, 2, 3, 4, 6

    2017

  33. [41]

    Learning a model of facial shape and expression from 4d scans

    Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6):194–1, 2017. 3

    2017

  34. [42]

    Photomaker: Customizing realistic human photos via stacked id embedding

    Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming- Ming Cheng, and Ying Shan. Photomaker: Customizing realistic human photos via stacked id embedding. arXiv preprint arXiv:2312.04461, 2023. 2

    2023 arXiv

  35. [43]

    Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching

    Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6517–6526,

  36. [44]

    Tada! text to animatable digital avatars

    Tingting Liao, Hongwei Yi, Yuliang Xiu, Jiaxiang Tang, Yangyi Huang, Justus Thies, and Michael J Black. Tada! text to animatable digital avatars. ArXiv, 2023. 2, 3, 6, 8

    2023

  37. [45]

    Headartist: Text- conditioned 3d head generation with self score distillation,

    Hongyu Liu, Xuan Wang, Ziyu Wan, Yujun Shen, Yib- ing Song, Jing Liao, and Qifeng Chen. Headartist: Text- conditioned 3d head generation with self score distillation,

  38. [46]

    Zero-1-to-3: Zero-shot one image to 3d object, 2023

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tok- makov, Sergey Zakharov, and Carl V ondrick. Zero-1-to-3: Zero-shot one image to 3d object, 2023. 3 10

    2023

  39. [47]

    Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age

    Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age. arXiv preprint arXiv:2309.03453, 2023. 3

    2023 arXiv

  40. [48]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 15

    2017 arXiv

  41. [49]

    Normal- ized avatar synthesis using stylegan and perceptual refine- ment

    Huiwen Luo, Koki Nagano, Han-Wei Kung, Qingguo Xu, Zejian Wang, Lingyu Wei, Liwen Hu, and Hao Li. Normal- ized avatar synthesis using stylegan and perceptual refine- ment. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 11662–11672,

  42. [50]

    Dreamer xl: To- wards high-resolution text-to-3d generation via trajectory score matching, 2024

    Xingyu Miao, Haoran Duan, Varun Ojha, Jun Song, Te- jal Shah, Yang Long, and Rajiv Ranjan. Dreamer xl: To- wards high-resolution text-to-3d generation via trajectory score matching, 2024. 3

    2024

  43. [51]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis, 2020. 1

    2020

  44. [52]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 3

    2021

  45. [53]

    Expressive whole-body 3D gaussian avatar

    Gyeongsik Moon, Takaaki Shiratori, and Shunsuke Saito. Expressive whole-body 3D gaussian avatar. In ECCV, 2024. 6

    2024

  46. [54]

    Arc2face: A foundation model for id-consistent human faces

    Foivos Paraperas Papantoniou, Alexandros Lattas, Stylianos Moschoglou, Jiankang Deng, Bernhard Kainz, and Stefanos Zafeiriou. Arc2face: A foundation model for id-consistent human faces. In Proceedings of the European Conference on Computer Vision (ECCV), 2024. 2, 3, 4, 5, 15

    2024

  47. [55]

    A 3d face model for pose and illumination invariant face recognition

    Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and sig- nal based surveillance, pages 296–301. Ieee, 2009. 3

    2009

  48. [56]

    Portraitbooth: A versatile portrait model for fast identity-preserved personalization

    Xu Peng, Junwei Zhu, Boyuan Jiang, Ying Tai, Donghao Luo, Jiangning Zhang, Wei Lin, Taisong Jin, Chengjie Wang, and Rongrong Ji. Portraitbooth: A versatile portrait model for fast identity-preserved personalization. arXiv preprint arXiv:2312.06354, 2023. 3

    2023 arXiv

  49. [57]

    Combining 3d morphable models: A large scale face-and-head model

    Stylianos Ploumpis, Haoyang Wang, Nick Pears, William AP Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scale face-and-head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10934–10943, 2019. 1

    2019

  50. [58]

    To- wards a complete 3d morphable model of the human head

    Stylianos Ploumpis, Evangelos Ververas, Eimear O’Sullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William AP Smith, Baris Gecer, and Stefanos Zafeiriou. To- wards a complete 3d morphable model of the human head. IEEE transactions on pattern analysis and machine intell...

    2020

  51. [59]

    Barron, and Ben Milden- hall

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

  52. [60]

    Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors

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

    2024

  53. [61]

    Gaus- sianavatars: Photorealistic head avatars with rigged 3d gaus- sians, 2024

    Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaus- sianavatars: Photorealistic head avatars with rigged 3d gaus- sians, 2024. 2

    2024

  54. [62]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  55. [63]

    Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. Generating 3D faces using convolutional mesh autoencoders. In European Conference on Computer Vision (ECCV), pages 725–741, 2018. 8

    2018

  56. [64]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2, 3, 4

    2022

  57. [65]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500...

    2023

  58. [66]

    Zeronvs: Zero- shot 360-degree view synthesis from a single image

    Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry La- gun, Li Fei-Fei, Deqing Sun, and Jiajun Wu. Zeronvs: Zero- shot 360-degree view synthesis from a single image. In Pro- ceedings of the IEEE/CVF Conference on Compute...

    2024

  59. [67]

    pytorch-fid: FID Score for PyTorch

    Maximilian Seitzer. pytorch-fid: FID Score for PyTorch. https://github.com/mseitzer/pytorch-fid ,

  60. [68]

    Splattingavatar: Realistic real-time human avatars with mesh-embedded gaussian splatting, 2024

    Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, and Zeyu Wang. Splattingavatar: Realistic real-time human avatars with mesh-embedded gaussian splatting, 2024. 2

    2024

  61. [69]

    Deep marching tetrahedra: a hybrid repre- sentation for high-resolution 3d shape synthesis

    Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler. Deep marching tetrahedra: a hybrid repre- sentation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems , 34:6087–6101,

  62. [70]

    Mvdream: Multi-view diffusion for 3d gen- eration

    Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d gen- eration. arXiv:2308.16512, 2023. 3

    2023 arXiv

  63. [71]

    Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion

    Jaidev Shriram, Alex Trevithick, Lingjie Liu, and Ravi Ra- mamoorthi. Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion. arXiv, 2024. 3

    2024

  64. [72]

    A 11 morphable face albedo model

    William AP Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B Tenenbaum, and Bernhard Egger. A 11 morphable face albedo model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5011–5020, 2020. 3

    2020

  65. [73]

    Denois- ing diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois- ing diffusion implicit models. In International Conference on Learning Representations, 2021. 4

    2021

  66. [74]

    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. arXiv preprint arXiv:2310.16818, 2023. 3, 6, 7, 8

    2023 arXiv

  67. [75]

    Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation. arXiv preprint arXiv:2309.16653,

    arXiv

  68. [76]

    Face0: Instantaneously conditioning a text-to- image model on a face

    Dani Valevski, Danny Lumen, Yossi Matias, and Yaniv Leviathan. Face0: Instantaneously conditioning a text-to- image model on a face. InSIGGRAPH Asia 2023 Conference Papers, pages 1–10, 2023. 2

    2023

  69. [77]

    Moa: Mixture-of-attention for subject-context disentanglement in personalized image gen- eration, 2024

    Kuan-Chieh Wang, Daniil Ostashev, Yuwei Fang, Sergey Tulyakov, and Kfir Aberman. Moa: Mixture-of-attention for subject-context disentanglement in personalized image gen- eration, 2024. 2

    2024

  70. [78]

    Imagedream: Image-prompt multi-view diffusion for 3d generation

    Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv preprint arXiv:2312.02201, 2023. 3

    2023 arXiv

  71. [79]

    Instantid: Zero-shot identity-preserving gener- ation in seconds

    Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, and An- thony Chen. Instantid: Zero-shot identity-preserving gener- ation in seconds. arXiv preprint arXiv:2401.07519 , 2024. 3

    2024 arXiv

  72. [80]

    Stableidentity: Insert- ing anybody into anywhere at first sight

    Qinghe Wang, Xu Jia, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, and Huchuan Lu. Stableidentity: Insert- ing anybody into anywhere at first sight. arXiv preprint arXiv:2401.15975, 2024. 2

    2024 arXiv

  73. [81]

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

    Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, et al. Rodin: A generative model for sculpting 3d digital avatars using diffusion. In Proceedings of the IEEE/CVF conference on computer vision and...

    2023

  74. [82]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score dis- tillation

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan LI, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score dis- tillation. In Advances in Neural Information Processing Sys- tems, 2023. 3

    2023

  75. [83]

    Srinivasan, Dor Verbin, Jonathan T

    Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Ho?y?ski. Reconfusion: 3d reconstruction with diffusion priors. In Proceedings of the IEEE/CVF Conference on Com-...

    2024

  76. [84]

    Flashavatar: High-fidelity head avatar with efficient gaussian embedding, 2024

    Jun Xiang, Xuan Gao, Yudong Guo, and Juyong Zhang. Flashavatar: High-fidelity head avatar with efficient gaussian embedding, 2024. 2

    2024

  77. [85]

    Fastcomposer: Tuning-free multi- subject image generation with localized attention

    Guangxuan Xiao, Tianwei Yin, William T Freeman, Fr ´edo Durand, and Song Han. Fastcomposer: Tuning-free multi- subject image generation with localized attention. arXiv preprint arXiv:2305.10431, 2023. 2

    2023 arXiv

  78. [86]

    Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians,

    Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians,

  79. [87]

    Facestudio: Put your face everywhere in seconds

    Yuxuan Yan, Chi Zhang, Rui Wang, Yichao Zhou, Gege Zhang, Pei Cheng, Gang Yu, and Bin Fu. Facestudio: Put your face everywhere in seconds. arXiv preprint arXiv:2312.02663, 2023. 3

    2023 arXiv

  80. [88]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. arXiv preprint arXiv:2308.06721,

    arXiv

  81. [89]

    Nofa: Nerf-based one-shot facial avatar reconstruction, 2023

    Wangbo Yu, Yanbo Fan, Yong Zhang, Xuan Wang, Fei Yin, Yunpeng Bai, Yan-Pei Cao, Ying Shan, Yang Wu, Zhongqian Sun, and Baoyuan Wu. Nofa: Nerf-based one-shot facial avatar reconstruction, 2023. 1

    2023

  82. [90]

    Inserting anybody in diffusion models via celeb ba- sis

    Ge Yuan, Xiaodong Cun, Yong Zhang, Maomao Li, Chenyang Qi, Xintao Wang, Ying Shan, and Huicheng Zheng. Inserting anybody in diffusion models via celeb ba- sis. arXiv preprint arXiv:2306.00926, 2023. 2

    2023 arXiv

  83. [91]

    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. 3

    2024 arXiv

  84. [92]

    Dreamface: Progressive generation of animat- able 3d faces under text guidance

    Longwen Zhang, Qiwei Qiu, Hongyang Lin, Qixuan Zhang, Cheng Shi, Wei Yang, Ye Shi, Sibei Yang, Lan Xu, and Jingyi Yu. Dreamface: Progressive generation of animat- able 3d faces under text guidance. ACM Trans. Graph., 42 (4), 2023. 6, 7, 8

    2023

  85. [93]

    Enhanc- ing detail preservation for customized text-to-image gen- eration: A regularization-free approach

    Yufan Zhou, Ruiyi Zhang, Tong Sun, and Jinhui Xu. Enhanc- ing detail preservation for customized text-to-image gen- eration: A regularization-free approach. arXiv preprint arXiv:2305.13579, 2023. 2

    2023 arXiv

  86. [94]

    Headstu- dio: Text to animatable head avatars with 3d gaussian splat- ting, 2024

    Zhenglin Zhou, Fan Ma, Hehe Fan, and Yi Yang. Headstu- dio: Text to animatable head avatars with 3d gaussian splat- ting, 2024. 2

    2024

  87. [95]

    Webface260m: A benchmark unveiling the power of million-scale deep face recognition

    Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Da- long Du, et al. Webface260m: A benchmark unveiling the power of million-scale deep face recognition. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and...

    2021

  88. [96]

    Mofanerf: Morphable facial neural radiance field

    Yiyu Zhuang, Hao Zhu, Xusen Sun, and Xun Cao. Mofanerf: Morphable facial neural radiance field. In European confer- ence on computer vision, pages 268–285. Springer, 2022. 3 12 Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance (Supplementary Mate...

    2022

  89. [97]

    Default Arc2Face Model: Using the default ID- conditioned Arc2Face model as the guidance model without any modifications

  90. [98]

    LoRA-Extended Model without View Embeddings: Using the LoRA-extended model but without view em- beddings, effectively setting the view embedding weight to zero (1 − b = 0)

  91. [99]

    Strong View Embedding Weight (1−b = 0.45): Using a strong weight for the view embedding to emphasize view information

  92. [100]

    Medium View Embedding Weight (1 − b = 0.3): Us- ing a medium weight for the view embedding, providing a balanced influence between identity and view

  93. [101]

    No LoRA No View Embs LoRA No View Embs LoRA View Embs (0.45) LoRA View Embs (0.3) LoRA View Embs (0.15) Ours Figure 11

    Our Method ( 1 − b = 0.15): Using the blending fac- tor we chose for our method, which we found to offer the best trade-off between identity preservation and view consistency. No LoRA No View Embs LoRA No View Embs LoRA View Embs (0.45) LoRA View Embs (0.3) LoRA View Embs (0.1...

  94. [102]

    library, which implements the Midpoint Subdivision algorithm. This process upscales the original mesh to Nupsampled = 79936vertices, with the majority concentrated in the facial area of interest ( Nface = 70033 vertices) and the remaining Nhead = 9903 vertices allocated to the...

    2000

Pith tools

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