Pith. sign in

REVIEW 4 major objections 5 minor 43 references

HumMorph: Generalized Dynamic Human Neural Fields from Few Views

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read HumMorph is a generalized feed-forward neural field that renders a human actor in any specified pose from one or a few monocular views, and is substantially more robust than prior generalized methods when body pose parameters are…

desk verdict Solid architectural contribution with a genuinely useful robustness evaluation scenario, but the headline robustness claim is only tested under one HybrIK noise distribution and needs either more evidence or a narrower claim. read the letter →

arxiv 2504.19390 v1 pith:OVES42Q3 submitted 2025-04-27 cs.CV

Jakub Zadrożny , Hakan Bilen This is my paper · ORCID
classification cs.CV
keywords generalizedhumanneuralradiancefieldsfree-viewpointrenderingnovelposesynthesisfew-shotconditioningmonocularvideovolumetricskinningweightcorrectionestimationrobustness
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

HumMorph claims that a single feed-forward model can render a human actor in any target pose from as few as one or two monocular observations, without per-subject optimization and without requiring accurate SMPL body-shape parameters. The paper argues that prior generalized human neural fields degrade sharply when body pose parameters are estimated rather than measured, and that HumMorph's dense 3D processing, which lifts 2D features into canonical-aligned volumes and learns a residual correction to heuristic skinning weights, absorbs that noise. On the HuMMan and DNA-Rendering datasets, with accurate parameters the method is competitive at one view and clearly better at two; with HybrIK-estimated parameters, its perceptual quality stays close to the quality of baselines given accurate parameters. If correct, this makes pose-controllable free-viewpoint human rendering practical from monocular phone video.

What carries the argument

The VoluMorph module is the central mechanism: it unprojects each observed view's 2D feature map into a 3D grid, undeforms that grid into the canonical T-pose using the forward LBS deformation $T_f$ (Eq. 1), then a 3D U-Net with cross-view attention aggregates the partial volumes into a complete canonical volume. A second pass of the same machinery predicts a log-space correction $\Delta W$ to the initial ellipsoidal-Gaussian skinning weights $W_0$ (Eq. 11), making the motion field that aligns views observation-conditioned. The same volume also yields a global latent code decoded into a triplane feature space ($f_{\text{glob}}$), voxel features ($f_{\text{vox}}$), and pixel-aligned features ($f_{\text{pix}}$) that an attention-based fusion module combines into the NeRF conditioning vector, with a nearest-bone regularization term $L_{\text{near}}$ (Eq. 6) encouraging natural deformations.

What would settle it

Run HumMorph with controlled pose corruption: take a test sequence, add increasing synthetic rotation noise to the estimated joint angles of the observed frames (e.g., 5, 10, and 20 degrees about random axes), and measure LPIPS and PSNR against the no-noise result. If there is a noise level below, say, 10 degrees at which HumMorph's perceptual quality drops to the level of SHERF or GHuNeRF given the same corrupted poses, then the claimed robustness is bounded rather than general; additionally, rendering from a target camera placed at a large angle from the observed cameras would test the claim of arbitrary target viewpoints.

Watch

Extended reading notes

Core claim

The paper's central discovery is that a generalized human neural field can be conditioned on a few monocular views by unprojecting per-view 2D features into partial canonical 3D volumes using a forward linear-blend-skinning deformation built from a heuristic initialization, then aggregating and refining those volumes with 3D convolutions and cross-view attention to produce a coarse canonical model plus a pose-conditioned correction to the skinning weights. The resulting coarse volume and global features are fused with pixel-aligned features through an attention module that decides which observed views are reliable for each query point, and the combined feature vector conditions a NeRF decoder. The authors claim this yields explicit pose control, fast feed-forward inference, and, unlike SHERF or GHuNeRF, graceful degradation when body pose is estimated with an off-the-shelf estimator such as HybrIK rather than measured by multi-camera rigs. They report that with two observed views the model's perceptual quality (LPIPS) markedly improves, and that with estimated body parameters its LPIPS remains competitive with methods fed accurate parameters.

Load-bearing premise

The learned correction can fix alignment errors only if the initial forward deformation, built from the fixed heuristic ellipsoidal-Gaussian skinning weights and the noisy pose, lands the observed features near their true canonical positions; if the pose noise is too large, VoluMorph cannot aggregate the views and the robustness claim fails.

Editorial extensions

If this is right

  • Free-viewpoint re-rendering of a person from two monocular frames becomes practical: the second view resolves the phantom-limb and occlusion artifacts that single-view methods imprint on the torso, with further gains saturating around three views.
  • Accurate multi-camera SMPL capture is no longer a prerequisite: because shape parameters are not required and the learned skinning-weight correction absorbs pose error, a monocular pose estimator like HybrIK can drive the renderer, with only a moderate drop in quality.
  • Inference is fast because it consists of a small number of feed-forward passes, so a novel subject can be animated without the long per-subject optimization typical of subject-specific human neural fields.
  • Pose control is explicit: the target pose is an input, so the same few observations can be re-rendered in arbitrary novel poses rather than only interpolating between observed ones.

Reading between the lines

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

  • The same dense-3D correction strategy could transfer to other articulated categories such as animals, robots, or deformable objects wherever a coarse kinematic skeleton and rough initial blend weights exist; the paper's experiments only cover human bodies in canonical T-pose space.
  • Robustness is demonstrated against one estimator, HybrIK, under a fixed training mixture; a controlled sweep of additive pose noise would reveal the failure threshold at which the heuristic initialization $T_f$ is too far off for VoluMorph to recover, making the robustness claim quantitative rather than binary.
  • The paper uses ground-truth camera poses and only tests target viewpoints effectively coincident with the observed cameras; extending to estimated cameras and genuinely different target viewpoints is the natural next test, and the multi-view geometry may turn camera error into the same kind of noise the volume aggregation already absorbs.
  • Because gains saturate by three views, pose diversity among the observations matters more than their count; actively selecting the most pose-diverse frames as conditioning inputs could extract more quality per view than adding near-duplicate frames.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces HumMorph, a feed-forward generalized human neural field that renders a previously unseen subject in a specified pose from one or a few monocular observations. It constructs canonical-space feature volumes with a VoluMorph module, refines heuristic skinning weights with an observation-conditioned correction, fuses voxel, global, and pixel-aligned features, and trains end-to-end with a perceptual loss. Experiments on HuMMan and DNA-Rendering compare against SHERF and GHuNeRF under both accurate and HybrIK-estimated body parameters, and an ablation study examines the contribution of each main component.

Significance. If the results hold, the paper makes a useful practical step: it removes per-subject optimization, avoids reliance on SMPL body shape parameters, and shows improved robustness to noisy pose estimates compared with SHERF and GHuNeRF. The end-to-end training on held-out subjects, the explicit comparison under estimated parameters, the component-wise ablation, and the detailed appendices are clear strengths. The core idea of dense 3D processing with a learned motion-weight correction is plausible, and the reported LPIPS gains are large in the main comparisons. However, the breadth of the robustness and free-viewpoint claims exceeds what the current experiments directly establish, and the absence of any variance estimates makes some of the smaller reported differences hard to interpret.

major comments (4)
  1. [§4.2, §C.1, Eq. (1)] The central practical claim — robustness to noisy body parameters — is supported by only a single noise condition. The estimated-parameter experiments use HybrIK estimates under a training mixture whose fraction p increases to 0.75 (Appendix C.1), and target poses are always accurate. Because the initial forward deformation in Eq. (1) is built from the heuristic weights W0 and is only corrected by the learned ΔW afterwards (Sec. 3.2), there is a plausible operating range beyond which the unprojection places features at wrong canonical locations and the correction cannot recover. A controlled sweep over pose-error magnitude (for example, adding calibrated rotation noise to joint angles) and a test with noisy target poses would be needed to substantiate the abstract's claim of graceful degradation. As it stands, the claim is demonstrated for HybrIK-level errors on observed frames only.
  2. [Tables 1, 3, 4] All quantitative results are single-run point estimates with no reported variance. Because several of the headline statements compare configurations with small differences (for example, Table 3 HuMMan PSNR: Ours 1-view 26.70 vs. SHERF 26.95; Ours 3-view 27.64 vs. 4-view 27.66), it is difficult to judge which differences are statistically meaningful. Reporting multiple seeds or confidence intervals, at least for the main tables, is necessary to support the 'significantly better' language used in the abstract and Sec. 4.3.
  3. [§4.2] The evaluation always uses observed frames from the same camera as the target frame. Although body rotation in the monocular sequences changes the body-relative viewpoint, the experiments never vary the target camera Eg relative to the observed cameras. Since the title and Sec. 1 claim free-viewpoint rendering and the method explicitly supports arbitrary Eg, a complementary experiment with target frames from held-out cameras (the datasets provide multi-view data) would directly validate the free-viewpoint aspect. Without it, the novelty over methods that only re-render from the observed camera direction is less clear.
  4. [Table 2] The ablation under estimated parameters reveals an interaction that is not discussed: adding fvox alone (the '+fvox' row) gives LPIPS 54.72, worse than '+ΔW' alone (51.62), even though the full model with both components is best (47.21). This suggests that the benefit of voxel features under pose noise is contingent on the motion-weight correction, which is exactly the regime where the robustness claim matters. The paper should discuss this dependency, as it bears on the claim that fvox 'can compensate for slight pose inaccuracies' (Sec. 3.1).
minor comments (5)
  1. [Eq. (6)] The notation B_k is used for a bone line segment but is never defined; please state that B_k is the segment between the two joints of bone k.
  2. [Sec. 3.2] The paper says 'we do not require the SMPL body shape parameters,' but the canonical skeleton J must still be obtained somehow. Please specify whether J is a fixed template, how it is chosen, and how it is obtained in the estimated-parameter setting.
  3. [Appendix C.1] The schedule for p is described as increasing linearly to 0.75 'at roughly half of the training process.' Please state the exact iteration count or schedule so that the training mixture is reproducible.
  4. [Figure 3] The VoluMorph diagram is dense; consider labeling the attention-based cross-view aggregation and the two output branches (feature volume and ΔW) more explicitly, as they are central to the method.
  5. [Sec. E] The paper states that code will be released publicly, but no repository URL or release plan is given. A URL in the revised version would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: HumMorph's few-view renderings are produced by a feed-forward network trained end-to-end on held-out subjects; no reported metric reduces to a fitted constant or self-citation.

full rationale

HumMorph's central claim is that a feed-forward model can render a held-out subject in a novel pose from one or two monocular views and degrades gracefully under estimated body pose parameters. The derivation chain is self-contained: observed frames are encoded into 2D feature maps F_t (Sec. 3.1); a first VoluMorph module computes a skinning-weight correction ΔW(I) from Eq. (1) with the heuristic initial forward deformation T_f_tilde, and Eq. (11) combines it with the heuristic W0; a second VoluMorph module computes voxel and global features using the resulting W; and the NeRF decoder is supervised with Eq. (5) (LPIPS, MSE, consistency, near-bone regularization) against ground-truth target patches. The initial W0 is an architectural prior from [36,37], not a fitted quantity encoding the test output, and it is explicitly corrected by a learned, observation-conditioned module. The reported tables evaluate on subjects and frames not seen in training, so the metrics are not re-displays of training fits. The robustness experiment trains with a mixture of accurate and HybrIK-estimated parameters (Appendix C.1) and evaluates with estimated parameters for observed frames; this is a train/test distribution match, not a fitted parameter renamed as a prediction. There are no self-citations and no imported uniqueness theorem that forces the architecture. The main limitation—that the initial T_f_tilde must be roughly correct and only HybrIK-level noise is tested—is a robustness/correctness concern, not circularity. Accordingly, no circular step is identified.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No new physical entities, forces, or conserved quantities are introduced. The VoluMorph module, triplane global latent, and skinning-weight correction volume are learned architectural components with falsifiable behavior only through the released renderings; they are not postulated entities needing independent evidence.

free parameters (3)
  • Loss weights lambda1, lambda2, lambda3 = 0.3, 2.0, 0.1
    Chosen by hand to balance LPIPS, MSE, deformation consistency, and near-bone regularization in Eq. (5); no tuning procedure or sensitivity study is reported.
  • Lconsis margin eta = 0.05
    Threshold in Eq. (10) below which the deformation consistency term is zeroed; chosen manually.
  • Estimated-parameter mixture schedule p = increases from 0 to 0.75 over the first half of training
    Probability of using HybrIK-estimated SMPL parameters during training (Appendix C.1); chosen by hand and affects how robust the model becomes to noisy inputs.
assumptions (6)
  • domain assumption Linear blend skinning with 24 joints and a learned weight field is an adequate deformation model for human bodies and clothing (Eqs. 2-4).
    The whole canonical-space representation and both deformations Tf and Tb rely on LBS; LBS is known to miss cloth and hair, and the paper acknowledges this in the limitations.
  • domain assumption Test-time body pose parameters and the canonical skeleton shape J are known with bounded error; only observed-view poses are noisy.
    Section 3 assumes Omega_t, Omega_g and J are known, and Sec. 4.2 always supplies accurate target poses and ground-truth cameras, so the method has never been tested with noisy target poses or estimated cameras.
  • domain assumption The heuristic ellipsoidal-Gaussian initialization W0 is close enough to the true skinning weights that VoluMorph can recover the rest.
    The unprojection in Eq. (1) uses Tf built from W0 before any learned correction is applied; if W0 is badly wrong, the feature volumes are misaligned before the 3D U-Net sees them.
  • domain assumption Training on synchronized multi-view datasets with a monocular protocol transfers to real monocular video.
    HuMMan and DNA-Rendering are multi-view captures; the monocular protocol samples observed and target frames from the same camera, which does not capture viewpoint diversity or real-world background clutter.
  • domain assumption HybrIK pose estimates are representative of in-the-wild pose estimation noise.
    Robustness to noisy parameters is demonstrated with one estimator under one mixture schedule (Appendix C.1); there is no measurement of a pose-error distribution or sensitivity across estimators.
  • standard math Volumetric alpha-compositing equations (7)-(8) are a standard rendering model.
    The rendering equations follow NeRF and are used without modification; they are accepted background rather than a new claim.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HumMorph: Generalized Dynamic Human Neural Fields from Few Views." pith.science (2026). https://pith.science/paper/OVES42Q3

@misc{pith2026250419390,
  author       = {Pith},
  title        = {Pith review of: HumMorph: Generalized Dynamic Human Neural Fields from Few Views},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OVES42Q3}},
  note         = {Machine review of arXiv:2504.19390}
}
read the original abstract

We introduce HumMorph, a novel generalized approach to free-viewpoint rendering of dynamic human bodies with explicit pose control. HumMorph renders a human actor in any specified pose given a few observed views (starting from just one) in arbitrary poses. Our method enables fast inference as it relies only on feed-forward passes through the model. We first construct a coarse representation of the actor in the canonical T-pose, which combines visual features from individual partial observations and fills missing information using learned prior knowledge. The coarse representation is complemented by fine-grained pixel-aligned features extracted directly from the observed views, which provide high-resolution appearance information. We show that HumMorph is competitive with the state-of-the-art when only a single input view is available, however, we achieve results with significantly better visual quality given just 2 monocular observations. Moreover, previous generalized methods assume access to accurate body shape and pose parameters obtained using synchronized multi-camera setups. In contrast, we consider a more practical scenario where these body parameters are noisily estimated directly from the observed views. Our experimental results demonstrate that our architecture is more robust to errors in the noisy parameters and clearly outperforms the state of the art in this setting.

Figures

Figures reproduced from arXiv: 2504.19390 by the authors.

Figure 1
Figure 1. HumMorph is a generalized method for free-viewpoint synthesis of humans in novel poses given a few observations. State-of￾the-art methods, including SHERF [11] and GHuNeRF [18], require accurate body pose annotations for the observed views. These are typically unavailable in practice and the poses need to be noisily estimated instead, like in the example above (poses shown in red). In this scenario, existing approac… view at source ↗
Figure 2
Figure 2. An overview of our approach. First, we extract the 2D featuremaps [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The architecture of our VoluMorph module. where Vt is the undeformed feature grid for view It with pose Ωt, xv is a 3D grid point, π(·, K, Et) is the camera projection operation with instrinsics K and extrinsics Et, and [·] is bilinear interpolation. The aligned, partial models (volumes) Vt are combined into a single, complete model V by a 3D U-Net-based convolutional network with attention￾based aggregation between… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison with SHERF (Mo) [ [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison with SHERF (Mo) [ [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Diagram of the unprojection and undeformation opera [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Extended qualitative comparison between our method, SHERF (Mo), and GHuNeRF on the HuMMan dataset. Numbers in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Extended qualitative comparison between our method, SHERF (Mo), and GHuNeRF on the DNA-Rendering dataset. Numbers [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Extended qualitative comparison between our method, SHERF (Mo), and GHuNeRF on the HuMMan dataset when using [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Extended qualitative comparison between our method, SHERF (Mo), and GHuNeRF on the DNA-Rendering dataset when [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 36 canonical work pages

  1. [1]

    imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose

    Thiemo Alldieck, Hongyi Xu, and Cristian Sminchisescu. imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 5441–5450, Montreal, QC, Canada, 2021. IEEE. 2

    work page 2021

  2. [2]

    Humman: Multi-modal 4d human dataset for versatile sensing and modeling

    Zhongang Cai, Daxuan Ren, Ailing Zeng, Zhengyu Lin, Tao Yu, Wenjia Wang, Xiangyu Fan, Yang Gao, Yifan Yu, Liang Pan, and others. Humman: Multi-modal 4d human dataset for versatile sensing and modeling. In European Conference on Computer Vision, pages 557–577. Springer, 2022. 2, 6, 12, 13

    work page 2022

  3. [3]

    Ef- ficient 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, and others. Ef- ficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 16123–16133, 2022. 4

    work page 2022

  4. [4]

    Geometry-guided progressive nerf for generalizable and efficient neural human rendering

    Mingfei Chen, Jianfeng Zhang, Xiangyu Xu, Lijuan Liu, Yu- jun Cai, Jiashi Feng, and Shuicheng Yan. Geometry-guided progressive nerf for generalizable and efficient neural human rendering. In European Conference on Computer Vision , pages 222–239. Springer, 2022. 2, 3

    work page 2022

  5. [5]

    Dna-rendering: A diverse neu- ral actor repository for high-fidelity human-centric render- ing

    Wei Cheng, Ruixiang Chen, Siming Fan, Wanqi Yin, Keyu Chen, Zhongang Cai, Jingbo Wang, Yang Gao, Zhengming Yu, Zhengyu Lin, and others. Dna-rendering: A diverse neu- ral actor repository for high-fidelity human-centric render- ing. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 19982–19993, 2023. 2, 6, 7, 8, 12, 13

    work page 2023

  6. [6]

    Comport, Srinath Sridhar, and Jean Martinet

    Arnab Dey, Di Yang, Rohith Agaram, Antitza Dantcheva, Andrew I. Comport, Srinath Sridhar, and Jean Martinet. GH- NeRF: Learning Generalizable Human Features with Effi- cient Neural Radiance Fields. In 2024 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition Work- shops (CVPRW) , pages 2812–2821, Seattle, W A, USA,

    work page 2024

  7. [7]

    MPS-NeRF: Generalizable 3D Human Rendering From Multiview Images

    Xiangjun Gao, Jiaolong Yang, Jongyoo Kim, Sida Peng, Zicheng Liu, and Xin Tong. MPS-NeRF: Generalizable 3D Human Rendering From Multiview Images. IEEE Trans. Pattern Anal. Mach. Intell., pages 1–12, 2024. 2, 3

    work page 2024

  8. [8]

    Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposi- tion

    Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Ot- mar Hilliges. Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposi- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2023. 2

    work page 2023

Show all 43 references
  1. [9]

    Unsupervised Learning of 3D Object Cate- gories from Videos in the Wild

    Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Ro- man Shapovalov, Tobias Ritschel, Andrea Vedaldi, and David Novotny. Unsupervised Learning of 3D Object Cate- gories from Videos in the Wild. In 2021 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR...

    2021

  2. [10]

    GaussianAvatar: Towards Realistic Human Avatar Model- ing from a Single Video via Animatable 3D Gaussians

    Liangxiao Hu, Hongwen Zhang, Yuxiang Zhang, Boyao Zhou, Boning Liu, Shengping Zhang, and Liqiang Nie. GaussianAvatar: Towards Realistic Human Avatar Model- ing from a Single Video via Animatable 3D Gaussians. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (C...

    2024

  3. [11]

    SHERF: Generalizable Human NeRF from a Single Image

    Shoukang Hu, Fangzhou Hong, Liang Pan, Haiyi Mei, Lei Yang, and Ziwei Liu. SHERF: Generalizable Human NeRF from a Single Image. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9318–9330, Paris, France, 2023. IEEE. 1, 2, 3, 4, 6, 7, 12, 13

    2023

  4. [12]

    Gauhuman: Articu- lated gaussian splatting from monocular human videos

    Shoukang Hu, Tao Hu, and Ziwei Liu. Gauhuman: Articu- lated gaussian splatting from monocular human videos. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 20418–20431, 2024. 2

    2024

  5. [13]

    Surmo: surface- based 4d motion modeling for dynamic human rendering

    Tao Hu, Fangzhou Hong, and Ziwei Liu. Surmo: surface- based 4d motion modeling for dynamic human rendering. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 6550–6560, 2024. 2

    2024

  6. [14]

    Neuman: Neural human radiance field from a single video

    Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, and Anurag Ranjan. Neuman: Neural human radiance field from a single video. In European Conference on Computer Vision, pages 402–418, 2022. 2

    2022

  7. [15]

    Analyzing and improv- ing the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improv- ing the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020. 4

    2020

  8. [16]

    Hugs: Human gaussian splats

    Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel, Oncel Tuzel, and Anurag Ranjan. Hugs: Human gaussian splats. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 505–515, 2024. 2

    2024

  9. [17]

    Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering

    Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs. Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering. In Advances in Neural Information Processing Systems , pages 24741–24752. Curran Associates, Inc., 2021. 2, 3

    2021

  10. [18]

    GHuNeRF: Gen- eralizable Human NeRF from a Monocular Video

    Chen Li, Jiahao Lin, and Gim Hee Lee. GHuNeRF: Gen- eralizable Human NeRF from a Monocular Video. In 2024 International Conference on 3D Vision (3DV) , pages 923–

    2024

  11. [19]

    Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation

    Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, and Cewu Lu. Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3383–3393,...

    2021

  12. [20]

    TA V A: Template-free Animatable V olumetric Actors

    Ruilong Li, Julian Tanke, Minh V o, Michael Zollh ¨ofer, J¨urgen Gall, Angjoo Kanazawa, and Christoph Lassner. TA V A: Template-free Animatable V olumetric Actors. In Computer Vision – ECCV 2022 , pages 419–436, Cham,

    2022

  13. [21]

    Vision Transformer 9 for NeRF-Based View Synthesis from a Single Input Im- age

    Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision Transformer 9 for NeRF-Based View Synthesis from a Single Input Im- age. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 806–815, Waikoloa, HI...

    2023

  14. [22]

    Hosnerf: Dynamic human-object-scene neural ra- diance fields from a single video

    Jia-Wei Liu, Yan-Pei Cao, Tianyuan Yang, Zhongcong Xu, Jussi Keppo, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Hosnerf: Dynamic human-object-scene neural ra- diance fields from a single video. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages ...

    2023

  15. [23]

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A Skinned Multi- Person Linear Model. ACM Trans. Graphics (Proc. SIG- GRAPH Asia), 34(6):248:1–248:16, 2015. Publisher: ACM. 2, 3, 7, 12

    2015

  16. [24]

    Generalizable Neural Human Renderer,

    Mana Masuda, Jinhyung Park, Shun Iwase, Rawal Khirod- kar, and Kris Kitani. Generalizable Neural Human Renderer,

  17. [25]

    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 synthe- sis. Commun. ACM, 65(1):99–106, 2022. 2, 3

    2022

  18. [26]

    TransHuman: A Transformer-based Human Repre- sentation for Generalizable Neural Human Rendering

    Xiao Pan, Zongxin Yang, Jianxin Ma, Chang Zhou, and Yi Yang. TransHuman: A Transformer-based Human Repre- sentation for Generalizable Neural Human Rendering. In 2023 IEEE/CVF International Conference on Computer Vi- sion (ICCV), pages 3521–3532, Paris, France, 2023. IEEE. 2, 3

    2023

  19. [27]

    ASH: Animatable Gaus- sian Splats for Efficient and Photoreal Human Rendering

    Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt, and Marc Habermann. ASH: Animatable Gaus- sian Splats for Efficient and Photoreal Human Rendering. In 2024 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR) , pages 1165–1175, Seattle, W A, US...

    2024

  20. [28]

    Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pa...

    2019

  21. [29]

    Ani- matable Neural Radiance Fields for Modeling Dynamic Hu- man Bodies

    Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Ani- matable Neural Radiance Fields for Modeling Dynamic Hu- man Bodies. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14314–14323, 2021. 2

    2021

  22. [30]

    3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting

    Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, and Siyu Tang. 3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5020–5030, 2024. 2

    2024

  23. [31]

    Com- mon Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction

    Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Com- mon Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), p...

    2021

  24. [32]

    Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data

    Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data. In Proceedings of the IEEE/CVF international conference on computer vision , pages 8863–8873, 2023. 4, 6

    2023

  25. [33]

    Barron, Ricardo Martin- Brualla, Noah Snavely, and Thomas Funkhouser

    Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srini- vasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin- Brualla, Noah Snavely, and Thomas Funkhouser. IBR- Net: Learning Multi-View Image-Based Rendering. In 2021 IEEE/CVF Conference on Computer Vision and Pat- tern Reco...

    2021

  26. [34]

    ARAH: Animatable V olume Rendering of Articulated Human SDFs

    Shaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu Tang. ARAH: Animatable V olume Rendering of Articulated Human SDFs. In European Conference on Computer Vision,

  27. [35]

    Schwing, and Shenlong Wang

    Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, and Shenlong Wang. GoMAvatar: Efficient An- imatable Human Modeling from Monocular Video Using Gaussians-on-Mesh. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 2059–2069, Sea...

    2024

  28. [36]

    Vid2actor: Free-viewpoint animatable per- son synthesis from video in the wild

    Chung-Yi Weng, Brian Curless, and Ira Kemelmacher- Shlizerman. Vid2actor: Free-viewpoint animatable per- son synthesis from video in the wild. arXiv preprint arXiv:2012.12884, 2020. 5

    2012 arXiv

  29. [37]

    Srinivasan, Jonathan T

    Chung-Yi Weng, Brian Curless, Pratul P. Srinivasan, Jonathan T. Barron, and Ira Kemelmacher-Shlizerman. Hu- manNeRF: Free-viewpoint Rendering of Moving People from Monocular Video. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 16189–1619...

    2022

  30. [38]

    pixelnerf: Neural radiance fields from one or few images

    Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578–4587, 2021. 2

    2021

  31. [39]

    MonoHuman: Animatable Human Neural Field from Monocular Video

    Zhengming Yu, Wei Cheng, Xian Liu, Wayne Wu, and Kwan-Yee Lin. MonoHuman: Animatable Human Neural Field from Monocular Video. In 2023 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 16943–16953, Vancouver, BC, Canada, 2023. IEEE. 2, 3, 4, 5, 6, 11

    2023

  32. [40]

    Efros, Eli Shecht- man, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shecht- man, and Oliver Wang. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 586–595, Salt Lake City, UT, 2018. IEEE. 5, 7...

    2018

  33. [932]

    1, 2, 3, 6, 7, 12, 13

    IEEE, 2024. 1, 2, 3, 6, 7, 12, 13

    2024

  34. [2022]

    Springer Nature Switzerland. 2

  35. [2024]

    2, 3, 12

    arXiv:2404.14199. 2, 3, 12

    arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.