SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Neural Image Representations and Explicit Meshes
Authors: Minje Kim
Tae-Kyun Kim
Venue: NeurIPS 2025 poster ()
Keywords: 3D Reconstruction
Multi-view Images
Super-resolution
Consistency
Primary area: deep_learning
TL;DR: 3D reconstruction of hand considering consistency from constrained resolution

Abstract: Reconstructing detailed hand avatars plays a crucial role in various applications. While prior works have focused on capturing high-fidelity hand geometry, they heavily rely on high-resolution multi-view image inputs and struggle to generalize on low-resolution images. Multi-view image super-resolution methods have been proposed to enforce 3D view consistency. These methods, however, are limited to static objects/scenes with fixed resolutions and are not applicable to articulated deformable hands. In this paper, we propose SRHand (Super-Resolution Hand), the method for reconstructing detailed 3D geometry as well as textured images of hands from low-resolution images. SRHand leverages the advantages of implicit image representation with explicit hand meshes. Specifically, we introduce a geometric-aware implicit image function (GIIF) that learns detailed hand prior by upsampling the coarse input images. By jointly optimizing the implicit image function and explicit 3D hand shapes, our method preserves multi-view and pose consistency among upsampled hand images, and achieves fine-detailed 3D reconstruction (wrinkles, nails). In experiments using the InterHand2.6M and Goliath datasets, our method significantly outperforms state-of-the-art image upsampling methods adapted to hand datasets, and 3D hand reconstruction methods, quantitatively and qualitatively. The code will be publicly available.

SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Neural Image Representations and Explicit 3D Meshes
Minje Kim
Tae-Kyun Kim
School of Computing, KAIST
fminjekim, kimtaekkun)@kaisst ac .r.
Abstract
Reconstructing detailed hand avatars plays crucial role in various applications. While prior works have focused on capturing high-fidelity hand geometry, they heavily rely high-resolution multi-view image inputs and struggle to generalize on low-resolution images. Multi-view image super-resolution methods have been proposed to enforce 3D view consistency. These methods, however, are limited static objects/scenes with fixed resolutions and are not applicable to articulated deformable hands. In this paper, we propose SRHand (Super- Sisper-Resolution Hand), the method for reconstructing detailed 3D geometry as well as textured images hands from low-resolution images. SRHand leverages the advantages of implicit image representation with explicit hand meshes. Specifically, we introduce a geometric- aware implicit image function (GIIF) that learns detailed hand prior by upsampling the coarse input images. By jointly optimizing the implicit image function and explicit 3D hand shapes, method preserves multi-view and pose consistency among upsampled hand images, and achieves fine-detailed 3D reconstruction (wrinkles, nails). In experiments using the InterHand2.66 MM and Goliath datasets, our method significantly outperforms state-of-the the-art image upsampling methods adapted hand datasets, and 3D hand reconstruction methods, quantitatively and qualitatively. Project page: httts::/yumiininn..grr ttttuub..o/projectt... srhand
Di Us Image
Full-body Captured Multi-viow Images 512)
Hägh-resoluiie Animatable Hand Avator
3D Hand Reconstruction from Low-res Imaggs
Figure 1: From low-resolution multi view RGB images, SRHand reconstructs highly detailed animatable hand avatars by super-resolving both images and 3D geometry.
1 Introduction
Human avatars play a crucial role in immersive human-computer interaction within virtual and augmented reality (VR/AR) environments. To provide realistic user experiences, it is essential to reconstruct the full body with high fidelity, both in geometry and appearance 22 Among
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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these. hands are particularly challenging due to their fine- theeggrined geometry, frequent occlusions, and high articulations. typical full-body capture systems, the hand occupies less than 1.5% of captured images, making high-fidelity hand reconstruction from low-resolution (I.R) regions difficult task as illustrated in Fig. T In multi-view setup, the resolution of the captured hand region varies with human poses and camera positions, making the reconstruction problem even more challenging.
While there are various studies 10 39 28 27 on the hand domain, achieving expressive hand avatars is an important task for realistic human representation. S2Hand 7 and AMVUR [8] reconstruct on the MANO model, and they suffer from blurry textures caused by the low resolution of their hand meshes. This issue is tackled by increasing mesh vertex resolution for clearer textures and more accurate geometry. MANO-HD (5). HARP (20), UHM [39]. and XHand [10] improve their hand model with surface subdivision, enabling to capture fine geometric details. However, they are heavily reliant on the high resolution input images from various viewpoints. LR images, shown in Fig.
lead to substantial loss of details in 3D reconstruction.
Increasing the resolution of LR hand images has not been studied as mainstream, up our knowledge. Super-resolution (SR) of generic images instead has been extensively explored [49 48 23] which aim recover high-frequency details from LR inputs. However, hallucinated textures and multi-view inconsistencies when applied to individual images stem from being used for multi-view 3D reconstruction. Several works [26 47
have tackled SR from LR multi-view images enforcing 3D view consistency using NeRF 36 and GS [21, but they are limited to static objects, fixed SR scales, non-respective on 3D shapes, and not appliable dynamic articulated targets like hands. Note variation of hand poses in frames makes the problem more challenging.
57 55
To address these limitations, we propose SRHand, novel framework for super-resolving hand avatars in both 2D images and 3D shapes from rimited-reoluttinn multi-view/pose images. We tightly integrate an image super-resolution module and 3D hand reconstruction module for high- fidelity avatar modeling. Considering the variation of captured hand region resolution, we propose Geometric-aware Implicit Image Function (GIIF), which super-resolves hand images to arbitrary scales, while being conditioned on surface normals. Leveraging ceometric-structuree representation from explicit mesh-based reconstruction, we optimize the SR module ensuring consistency across varying view and pose images. This joint formulation removes blurred, overbounded shapes, and flickering artifacts and maintains accurate 3I) structure across poses and viewpoints. We validate SRHand on real-world datasets, demonstrating significant improvements in appearance and geometry quality compared to existing SR methods adapted to hand images and 3D hand reconstruction baselines. The framework enables reconstructing hand avatars with wrinkles, nails, and subtle shape variations, even from low-resolution image inputs, which is essential for realistic, interactive VR/AR applications.
In summary, our main contributions are as follows:
We introduce SRHand, a novel framework that super-resolves both 2D images and 3D shapes by integrating implicit image representations with explicit 3D meshes from LR images.
We propose a geometric-awand implicit image function (GIIF), that conditions implicit neural representations normal maps from template model while leveraging adversarial learning to enhance texture fidelity.
We jointly fine-tune the hand SR module with the 3D reconstruction process to enforce multi-view/pose consistency.
Related Work
3D Avatar Reconstruction. Human avatar reconstruction has been widely explored using various
representations, including implicit fields such as neural radiance fields (NeRF) 36
T7
53 5].
Gaussian Splatting (GS) 25 [6 38 44, and explicit meshes [56] 50 52
18
24
39
3
While NeRF
and GS approaches excel in producing peoto-realistic renderings, they often require dense viewpoints and struggle to represent accurate geometry. In contrast, based methods explicitly represent geometric structures but are highly dependent on 3D resolution for optimal representation.
39 1Q avatars using explicit mesh-based have been proposed to represent hand appearance based on UV texture map rendering. UV texture rendering provides higher texture details,
Previous studies have achieved expressive hand 3
20
24
methods. Specifically, several works 20
24
39

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even with low vertex resolutions. 12nV-HandNet [3] focuses on reconstructing a high-resolution UV-map hand mesh without texture from a single image. However, these approaches fall short of representing fine-grained 3D geometrie details. In contrast, vertex color-hased representations have been studied in 18 10.. Due to the fixed resolution constraint, S2Hand (7 and AMVUR [8] suffer from blurry textures caused by the low resolution of their hand mesh (778 vertices). Several works have been explored to upsample hand meshes. OHTA (62) utilizes MANO-HD (12,337 vertices) from Avad.vvtarr [5]. and URHand [8] relies on mesh model proposed in UHM [39 (15,930
vertices). Following XHand [TQ), our mesh representation comprises over 49,000 vertices and 98,000
faces, enabling the capture of fine geometric details. In this way, our mesh representation narrows the performance gap with UV-based texture rendering methods while surpassing geometric-level details representation. However, XHand still falls short in capturing detailed geometrie shapes on hand meshes since it does not consider whether the visual appearance is caused by texture colors or geometric shapes. Our mesh representations better model finer details by learning the geometric appearance and the texture color appearance. All aforementioned methods remain highly sensitive to input image resolution. LR images lead to degraded texture fidelity and loss of fine geometric detail, limiting performance.
Image Super Resolution. Super-resolution (SR) of generic images has been extensively studied. Since SR is kind of generative method, there exists a distribution of possible SR results from LR images. While many research [4 48 23 have been proposed, we employ implicit neural representation methods as those methods are spatially adaptive to the 3D surface and capable of controlling image resolution per need while achieving plausible results. Local Implicit Image Function (LIIF) (6), which is the most representative work in 2D space, proposes to represent images images. This approach climinates
in the continuous space, allowing it to generate the need for retraining when scaling to arbitrary magnification factors (e.g.. x4, x12.8), offering unified solution for diverse super-resolution tasks. Building on implicit image functions, LIIF-GAN [19] introduces adversarial learning to enhance sharper textures and more realistic details. We enhance the fidelity of hand neural representations by leveraging normal maps derived from the MANO [45 template, while preserving realistic textures and ine-grained details through adversarial learning.
Multi-view Super Resolution with 3D Consistency. Previous works B1
33
34
55
58
46) propose to leverage generative 2D image prior using denoising diffusion 15| to reconstruct 31)
objects from single or multi-view images. Especially in [55 58 32 multi-view consistency problems are addressed using confidence obtained from 3D space. Recent works 26
55
57 g have attempted
to integrate image super- super-resolution techniques with NeRF for 3D view consistency. DiSR-NeRF [26] exploits a pre-trained diffusion model to reconstruct details that are missing in a low-resolution NeRF (LR NeRF) optimization. DiSR-NeRF is dependent on prompt guidance, otherwise the reconstructed details deviate from the ground truth. Yoon et al. (55) refines SR images by aggregating features from the SR module, NeRF representation, and uncertainty estimates of NeRF. While effective, this method relies on several pre-trained components which limit its adaptability. SuperNeRE [57) generates high-freug frequecy details from LR NeRF consistency by searching the latent space of ESRGAN 44 for view-consistent solutions. Yet, enforcing multi-view consistency in the LR domain does not guarantee that obtained nigh-frequency details remain consistent across views. SRGS (9] utilizes the Gaussian splatting (GS) (21] method, which is known to be effective for rendering static scenes. However, GS well known to fit will in static scenes and likely lose geometric details.
The aforementioned works have attempted to preserve multi-view consistency between super-resolved images for static scenes. None of these approaches has effectively tackled the challenge of preserving consistencies for dynamic, deformable objects, i.c. pose as well as view consistency. They are mainly for rendering novel view images, not offering explit 3D shapes. Besides, the required number of images dramatically increases for reconstructing animatable avatars, increasing computational costs and complexities. Our method overcomes these limitations by fine-tuning the SR module, enforcing multi-view and pose consistency while enhancing the fidelity of fine details in both images and 31) shapes, making our approach suitable for dynamic hand avatars.
Methods
We propose SRHand, a method for reconstructing a detailed hand avatar in both 2D and 3D represen- tation. Fig. 2e.22sshon shooo the overall framework of SRHand. Sec. 3.1 explains the SR module which is
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Figure 2: (a) Given LR images, we reconstruct high- resolutioo images using GIIF. Using these images, we reconstruct detailed 3D shapes while jointly optimizing the GIIF through adaptive fine-tuning from 3D shapes. (b) shows the architecture of GIIF. (c) represents the adaptive fine-tuning process.
geometric-aware implicit image function, Sec. 3.2 describes learning explicit hand representation, and Sec. 3.3 gives the details of 3D shape learning with adaptive fine tuning of the SR module.
3.1 Learning Implicit Image Function
The implicit image function interprets images as coordinate-bast neural functions, enabling resolution-agnose inference [6] Its image function gets sampled encoded features using pixel coordinates x, and cell shape features realize the query position of the overall shape cells. The implicit function formulated as:
Preliminary.
LIIF()+) fe (z, [z, c)), where (Itr) where Eg represents a low- row-uusouution image encoder parameterized with yp, and [cA cwl contains two values specifying the height and width of query pixel.
(I)
Geometric-aware Implicit Image Function. Shown in Fig. 2 bottom left, focusing on hand image SR, we propose a scometric-awart implicit image function (GIIF) that leverages surface normal guidance to recover high-fre feegh-freuencyy texture details via continuous image representations. Inspired by SuperNeRF-GAN [61], which integrates geometric priors for enhanced SR, GIIF utilizes normal map derived from the MANO 45 template hand model. These normal maps provide structural priors hand articulation, enabling robust reconstruction of fine-graines geometric details. Normal embedded features are then concatenated with LR image features, which are represented continuously through linear layers parameterized with with pixel coordinates queries.
Given a LR hand image lir, GIIF G extracts hierarchical features using Residual Dense Network (RDN) encoder & (parameterized by sp) [60] In parallel, the normal map N extracted from the MANO 45 is encoded via a stacked hourglass encoder Eg (parameterized by vp) 42 which is known effective for multi-scale feature extraction. The fused feature ffused is obtained through:
f fused & (1r)). @ (N)
(2)
where denotes channel-wise concatenation. Following LIIF (6), continuous coordinate queries are mapped to RGB values via linear layers Fo (paramcterized by 0):
Isr G(Urr, NN FA (ff (x)
To enhance photorealism and sharper details, adopt adversarial training loss LGAN with discriminator D. The GIIF training loss functions are:
Liotal A1Ci ALPIPSCLPIPS AGAN
(4)

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3.2 Learning Detailed Explicit Shapes
Preliminary. XHand [0]] has achieved expressive hand avatar reconstruction from 2D high- resolution images. XHand subdivides MANO [45] mesh (M) 3 times, denoted as M', increasing the resolution of the mesh. This approach enables the explicit shape to capture finer detailed geometric shapes. With subdivided hand meshes, XHand optimizes Linear Blend Skinning (L.S) weights W. delta values D to calculate vertex displacements, and albedo colors p from feature embedding modules 2 (.W).p) from given input hand joints J. XHand is formulated as:
(5) where 0 and denote the pose and shape parameters for template mesh f2, respectively. x' is the camera parameter on i-th viewpoint, denotes albedo, SH denotes spherical harmonics function with Y coefficients, and N the normals rendered from posed mesh M.
xx = R(= Pp(J) -H,(H,Y(Y))) where M = Q(M' + ΦD(J), 4 (J),0
Enhancing Geometric Details on the Explicit Shape. We adopt high-resolution mesh rep- resentation that enables fine-grained geometric modeling of the hand surface. Our delta feature embedding module $ incorporates the predicted color to disentangle geometric deformations from texture-driven appearance. To facilitate, we add the mean texture loss function to the training loss functions. Acquiring mean textures removes the appearance caused by texture color and encourages the model to learn geometric appearances. Our 3D representation formulated as:
Mfnne = I(M' (J)),, (J),),,,
3.3 Overall Pipeline.
Shown in Fig. 2 we first train GIIF learn the implicit image pri- ors of hand appearances through di- verse hand subjects. After train- ing GIIF, we get highly detailed im- ages from given low-detailed im- ages. However, similar to prior works [54 57 55 26), GIIF itself does not guarantee 3D consistencies. To preserve 3D consistencies, we propose to fine-tune the SR model with 3D reconstructed models.
Algorithm 1 Psuedocode of SRHand fine-tuning.
Require: Dataset of Ir. J and N, pre-trained GIIF Gy.pp and discriminator D, 3D reconstruction networks & involv- ing subdivided template mesh M', T total epochs. Time total fine-tuning epochs, fine interval steps for fine-tuning Personalized &w.D.p and G
Ensure:
I: 1 + G(Irr, N)
Initially upscale 4tr images
2: for t= 01o do
{f'M (w.
4: Compute "aD(i'M'' finer ( (Eq. 7
5:
Gradient step to update PWPD,
if mod fine
then
Fine-tuning stage
Adaptive Fine-tuning. Guo al. 122 mathematically analyzed fine- tuning generative diffusion models
7:
for = 0 to Tyinc do T + I(Trr)
8:
9:
Compute Eafe(Tirr 10. M'
fine )Eq. 10
with guided self-generated images
10:
Gradient step to update T.o
achieve global optima. We adap- 11:
end for 1t+1 <- L(Irr)
tively fine-tune GIIF with a recon- 12:
D Update the SR image
structed 3D shape trained on im- 13:
else
ages generated by GIIF itself. As
14:
1141 f- 15
P- If not, maintain L
the 3D reconstruction model inher- 15: end if
its consistencies across multi-view 16:
end for
pose diversities, iterative fine-tuning GIIF shifts its distribution to the consistency maintained space. We show the pseudo code of our and the loss functions are introduced in Sec. 3.4.2
SRHand fine-tuning in Alg. I
3.4
Training Objectives
3.4.1
3D Geometrie Loss.
Our loss function for learning 3D geometry includes photometric loss Ligb to minimize rendering errors via inverse rendering and a regression loss Lreg for hand geometry. Ligs consists of LI and perceptual loss between rendered image and SR image Ler. Creg consists of part-aware Laplacian
5

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smoothing LpLap and edge regression loss Ledge used in [T]. CpLap is Σ, OpLapA applied to both albedo p and displacements D, where @plap is part-level weights and Laplace matrix. Ledge is Eig Ilé. - eij 12. where Eij euclidean length between adjacent vertices V4 and Vj the refining mesh Mine, and ēιj is the corresponding edge length in the initially subdivided mesh M'
Mean Texture Loss and Total Loss. To enhance details for geometric appearance, apply the mean texture loss function Lms utilizing the perceptual loss to capture fine details. LaD is defined as:
C3D
Lrgb + Lreg Am (LPIPS(((T))))
where D represents the mean texture of albedo colors.
3.4.2 Adaptive Fine-tuning Loss.
Our adaptive fine-tuning loss function consists with three parts; the consistency loss Leans, frequency maintenance loss Lwaves and discriminator loss fasse--
Consistency Loss. The consistency loss aims to minimize the variation of the vertex queries Q IR Nx3 between multi-view and multi-pose images Isr- We first calculate the mean 14 and variance Ps of the given each queries from multi-view images at sequence t. After earning μex for all Q, we create an unconfidence map (Q). Pose consistencies are indicated as subtraction of μ with previous step 14-1 and multi-view consistencies are presented as variances. Having (Q( as (ju: 14-1) + Pv: we formulate Loons as:
Leons ((((((M (Q), (Q)))
(8)
Frequency Maintenance Loss. Applying Lcons leads to the mean texture value losing high- frequency details. To maintain total high-freyuency details, adopt Lwave as:
Lwave L1 (o(1))) o(L2))) + f. E E((()))
where ` and @ denote discrete wavelet transform functions with low-pass and high-pass filters.
Discriminator and Total Loss. To improve photometric reality, we propose to use the discriminator D trained with GIIF. We include the discriminator loss Ldisc to the total adaptive fine-tuning loss function Cast. which described as follows:
Last
Loone + Lwave + Ldiso where Ldise logll1l ))
(10)
4 Experiments
4.1 Datasets and Experiment Settings
InterHand2.6M 40). We mainly use the InterHand2.6N, (40] dataset for super-resolution and
3D reconstruction experiments. The dataset presents diverse views of cameras with a single hand and
Table 1: Quantitative comparisons between compared methods using InterHand2. 6M 40 and Goliath (35] dataset. PSNR LPIPS (SR) shows the PSNR and LPIPS performance of the super-resolution modules. Mark "Incon." stands for inconsistency and "ftd." stands for the fine-tuned model. The top three results are highlighted red, orange, and yellow, respectively.
Recon.
InterHand2 4M
Module
Methods
PSNR /LPIPS (SR)
PSNR
1PPIS
P2P (mm)
Incos.
PSNK /IPIPS (SR)
PSNR
TPPIS
P2P (mar)
Incon.
Bicubic
Ours
22.23 0.2645
26.44
0.0895
4.01
0.0131
0..17
22.53
0.1377
5.80
0.0128
XHand [10]
0.0146
LII
Ours
27 10.663
27.11
0.0755
3.39
0.0511
24.87 0.1459
22.87
0.1123
4.12
0.0140
GIF
UHM [9]
29.96 /003US
22.33
0.0507
0.1522
72.55
0.0067
23.85
0.1319
24.29
0.0844
0.0404
3.49
GIF
XHand Ours
30.03 / 100330
28.75
0.0113
3.45
0.0052
28.07 / 0.0195
21.95
0.1
3.60
0.0082
Dd.)
30.06 /00030
29.88
0.0362
2.16
0.0050
28.09/ / 0.0195
24.31
0.0813
3,50
0.0069

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Figure Qualitative results of 3D reconstructions. Given low-resolution (low-detailed) images, SRHand reconstructs high-resolution personalized hand meshes.
Two hands of different identities. For SR experiments, we divide train and validation data based capture subjects to guarantee robustness across subjects in the "train" split. For 3D reconstruction experiments, we used CaptureO and Capturel in the "test" split following prior works 41 I3 10
We reconstruct 3D hands with 20 views and 20 frames and evaluate with the remaining frames.
Goliath [35 dataset has similar experiment settings with InterHand2.66 [40], how- ever, it presents scanned mesh and high-resolution images. However, as it only presents only subjects, which is insufficient for training the SR module, we used the SR module trained on Inter- Hand2.6M. More details on preprocessing the Goliath dataset are presented in the supplementary.
Goliath (35)
Metrics. For the quantitative comparisons of different appearance models, we use set of metrics that are often applied to assess the fidelity and quality of rendered images. We mainly use the learned perceptual image patch similarity (LPIPS) [59], the structural similarity metric (SSIM) [51], and the peak signal-to-noise ratio (PSNR) for comparison metrics. We also present Point-to-Pone Point (P2P) of reconstructed meshes against the ground truth meshes. Since InterHand2.66
[40] does not provide scanned meshes, trained XHand [10) 50 frames from 50 viewpoints using high-resolution ground truth images. The obtained meshes serve as our pseudo ground truth for P2P evaluation.
4.2 Compared Methods
We conduct experiments in three parts to investigate the efficiency of our proposed framework: (1) 3D Reconstruction from SR Images, (2) 3D Consistency Considered Super- Resolutin,, and (3) Hand Image Super-Resooutton.
3D Reconstruction from SR Images. Our method leverages 3D appearances for both 3D represen- tation and SR module fine-tuning. As shown in Tab. D we evaluate 3D reconstruction performance and fine-tuned SR model across different alternative methods including UHM [39]. For both In- terHand2.66 40 and Goliath (35) datasets, reconstruction results are reported in terms of PSNR, IPIPS for visual appearance, and point-to-point (P2P) distance for geometric shapes. The inconsistency is quantified using aggregated variations across reconstructed vertices.
Fig. 3 and Tab. show the experimental results on the InterHand2.6M [40] and Goliath [35] datasets. Our hand mesh better represents hand shapes than the compared methods. Specifically, UHM 39 fails to represent hand shapes and textures, losing details and including background artifacts. Simply attaching GIIF and XHand (IO] results overbounded shapes due inconsistencies of hand shape and details in the SR images. Furthermore, the results demonstrate that fine-tuning the SR module enhances 3D reconstruction performance compared to its original state. This improvement highlights the effectiveness of adaptive fine-tuning in enforcing consistency for more accurate 3D reconstruction while improving photometric quality, as shown by the PSNR/L.PIPS (SR) metric. In conclusion, HRHand achieves the best overall performance, outperforming all tested configurations.

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Table Quantitative comparisons among 3D consistency considered SR methods, (x) denotes an upscaling factor. Bold and underline denote top two scores.
Upscale factor
Methods
PSNR
LPIPS
SSIM
NeRF-SR
47
28.83
0.1410
0.8638
DiSR-NeRF 26
28.82
0.1303
0.8736
x4
SRGS (
32.74
0.0731
0.9335
SRHand
29.06
0.0475
0.9016
SRGS
29.22
0.1520
(.8739
x8
SRHand
28.86
0.0487
0.9007
SRGS
27.18
0,3872
0.7899
x16
SRHand
27.52
0.0566
0.8858
SR0S 11 Sasoooae in serre S0 Li Oecurd Frath
Figure 4: Qualitative results. Red circles show background artifacts GT data. All baselines are compared with upscaling factor 4.
3D Consistency Considered Super-Resolution We compare SRHand with NeRF-SR DiSR- NeRF (26] and SRGS [9] for 3D consistency considered super-resolution. Prior works are restricted to reconstructing static objects rather than human avatars, thus we train our method with only single pose assuming it static scene with multi-view images for fair comparison. In addition, prior works are also restricted to X 4 scale upsampling, we present our results with x and x 16 upscaling results. SuperNeRR [57] and Yoon et al. (55) are omitted due to the unavailability of source codes and lack of implementation details. However, note DiSR-NeRE shows better performance than SuperNeRF, reported in (26).
Grvund Tnuth
Luw Roulution (Bicubik)
Real- ESRGAN [48]
Swinlk [10]
LUB
LOF
IDM
Figure 5: Qualitative comparisons among SR modules. Original IDM results are in the supplementary.

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Table 3: Quantitative comparisons of SR modules on InterHand2.66 6M
40)
(a) Experiments are performed with x 16 upscaling. (b) Results in (PSNR / LPIPS) of continuous scale (" denoles the model has been modified with normal trained on x 16 factor. GIIF achieves best perfor- map conditioning.)
mance in all scaling factors.
Methods
PSNR
LPIPS
Upscaling factor
Methods
Real-ESS ESRGAN
22.39
0.2287
x21.3
x32
SwinIR 30
25.51
0.1552
LUIF 6
28.62/0.1319
23.91/0.2071
21.46/0.2693
LII 6
25.76
0.1848
IDM
14.58
0,3603
IDM II
25.46/0.1214
21.28/0.1796
19.02/0.2426
LIIF"
29.85
0.0996
LUFF
30.20/0.0812
29. 29.17/0.0855
Geometric-
IDM
21.49
0.0970
aware
GIIF
31,60
0.0637
IDM
22.68/0.0696
22.71/0.0780
22.87/0.0878
GIIF 32.70/.0053
31.53/ 0.0606 30.01/0.0640
Tab. 2 shows quantitative results compared with NeRF-SR 47. DiSR-NeRF (26 and SRGS [9] trained on 40 view images. SRHand supports arbitrary-scale super-resolution and outperforms prior methods, even at 16 times upscaling factor. Fig. 4sshww the qualitative results and illustrates that method captures fine hand features compared prior works. NeRF-SR and DiSR-NeRD still suffer from blurriness and appear overly synthetic. Considering that GT data and previous works often include background colors the hand, method eliminates these artifacts. Despite having differences from GT' data, our approach still achieves superior performance.
Hand Image Super-Resolution. Resolution. We compare GIIF with existing image super-resolution models [88 30 In. We retrained all the baselines to the hand image datasets and extend LIIF [6] to geometric-aware LIIF (denoted LIIF)) and IDM [11] geometric-aware IDM (denoted IDM") for a fair comparison. Tab. [3a shows the quantitative comparisons and Fig. shows the qualitative results. GIFF significantly achieves better results with
realistic hands with detailed appearances. Comparing LIIF with LIIF* and IDM and IDM*, we observe that the normal conditioning substantially improves rendering quality, enabling part-aware generation. Furthermore, the discriminator refines photometric details and realism. Tab. [3 shows that our method achieves the highest performance at arbitrary upscaling factors, showing robustness across in-distribution and -uttof-diitributtion scales.
Table Ablation study of geometric enhancements and adaptive fine-tuning loss functions.
(a) Ablation study on 3D hand reconstruction meth-(h) Ablation study on the adaptive fine-tuning loss func- tions.
ods using identities.
PSNR
LPIPS
SSIM
Leanr
Lucnu
Ldtte
PSNR (SR)
PSNR
I.PIPS
P2P (num)
XHand 10
27.77
0.0423
0.8977
29.96
29.17
0.0404
3.09
Identity
23.94
23.39
0.0864
3.72
Ours
28.92
0.0353
0.9089
/
30.06
29.88
0.0362
2.16
XHand
28.72
0.0338
0.9125
Identity 2
Ours
29.42
0.0303
0.9189
4.3
Ablation Studies
Geometric Enhancements. We conduct an ablation study to compare our enhanced mesh repre- sentation with the baseline XHand [TO]. As shown in Tab. 44 and Fig, 6b, our representation better captures fine details while consistently achieving higher performance across all metrics in different identities.
Effectiveness of Adaptive Fine-tuning Loss. We show the ablation study of adaptive fine-tuning loss. Tab. 4b and Fig. [6 represent that mesh distance error and texture inconsistencies decrease when adaptive fine-tuning is applied.
Consistency Loss. We evaluate the impact of various loss components during fine-tuning, shown in Fig. 6p. and Tab. 4b Applying the consistency loss (Jeoms) degrades the performance leading overall hand textures to a mean value. However, regarding Fig. consistencies are well maintained presenting the mean texture.

---

Table Quantitative results of showing the effectiveness of GT parameters.
ResoluReso
3D Reconstruction
MPJPE (mm)
PSNR
LPIPS
PSNR
LPIPS
Non-using GT Param. Using GT Param.
9.54
30.11
0.0306
27.18 29.88
0.0635
30.06
0.0302
0.0362
Frequency Maintenance Loss. The wavelet loss (Lwavee plays vital role in preserving multi-scale details in the upsampling and reconstruction process. As shown in Tab. 4b and Fig. 6p. its inclusion alongside the consistency loss significantly enhances visual quality by maintaining fegh-frryeeccy components. This demonstrates that the frequency maintenance loss is effective for maintaining structural details and fine textures.
Discriminator Loss. Adding the discriminator loss (Ldisc) further refines the output by pro- moting photometric consistency and realism.
When integrated with Leons and Luaves Ldisc improves both quantitative and qualitative met- rics. This adversarial component enhances the generation of realistic textures, as shown in Tab. 4b and Fig. 6b.
Skibeed
Valla
Non-using GT parameters. Although using ground-truth (GT) template parameters is a widely adopted strategy in 3D hand and body
Vinakiaton locesicnny
avatar reconstruction (5 [29 I0 62 (77) and our method follows the same strategy, we addi- tionally provide results where noise is added the GT parameters. Tab. 5 shows that MANO parameter errors do not have critical effect on super-resolution performance, while they affect the quality of 3D hand reconstruction.
Aooe 1 Aoee
fooe Avner Sau
Adythe
Conclusions
Niluad
(6 Courvett Pdvvecemet
In this paper, present SRHand that inte- grates view/pose-awer aware implicit neural repre- sentations with explicit 3D mesh reconstruc- tion for high-fidelity hand avatar modeling. Our approach leverages geometric-aware implicit image function (GIIF) to super-resolve low- detailed hand images in arbitrary scale while maintaining 3D view/pose consistency through jointly fine-tuning the SR module and 3D recon-
Figure 6: Visualization of ablations studies: (a) Inconsistencies and mesh distance enror Mark "aft." stands for adaptive fine-tuning. (b) Laft loss function. To better highlight the difference. we adjust brightness and contrast. (c) Enhancing geometric details.
struction. Extensive quantitative and qualitative results using InterHand2.6M 40 and Goliath 35 datasets demonstrate that our method efficiently captures fine details in both 2D images and 3D shapes of hands, and outperforms the baseline methods. We anticipate that our approach can be applied to wider scope avatar creation, providing users with a realistic experience virtual environments and accurate 3D human reconstruction.
6
Acknowledgement
This work was supported by NST grant (CRC 21011, MSIT), IITP grant RRS-2023-0022899 RS RS-2024-00455974, 05--2025-25443333 00442308, MCST).
MSIT) and KOCCA grant (RS-2024-
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Reviews:
[{"review_id": "ZE3FYZcM9q", "reviewer": "Reviewer_yhNh", "summary": "The authors introduce SRHand, a method to reconstruct detailed 3D geometry and appearance of hands in the setting of low-resolution images. The key insight is to use an implicit image representation (GIFF) to learn prior information on hands. Experiments show superior performance in hand image super resolution. They also compared to hand avatar creation methods but not very comprehensive.", "questions": "- Compare with more valid baselines in 3d avatar creation\n- In the final version, adjust the story and focus on 3d avatar creation.", "limitations": "The authors should also discuss about the negative societal impacts. For example, it maybe a privacy concern if the avatar encodes biometric identifiers (e.g., hand appearance, geometry and motion patterns), which can affect surveillance.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Pros\n\nThis is a very novel and practical task. Human hands are often very small in full-body images settings. Addressing the low resolution problem is very useful for large-scale reconstruction for in-the-wild videos.\n\nImage resolution results look very impressive compared to the baselines. It contains detailed appearances such as nails and wringles. \n\nCode will be available for research.\n\n## Cons\n\n**Insufficient valid baselines**: Most efforts of the experiment section is on evaluating super resolution. The evaluation is comprehensive and impressive. However, this is not the case for the main focus of the paper, hand avatar creation. Table 3 compares with UHM, and XHand but did not compare with existing baselines such as HARP [2], LiveHand [3], and Handy [4]. In particular, LiveHand also uses a super resolution method and it seems relevant to compare. Further, XHand is an arxiv and unpublished paper. It is not really a valid baseline.\n\n**Unclear research goal**: The paper is motivated by about AR/VR and how the creation of hand avatar can help immersion (L20-22). The experiments are splitted into two main parts: hand image super-resolution and 3D hand avatar creation, where the former is emphasized a lot more than the latter. I think it is easier for the readers if the story centers around 3D hand avatar creation and use the super-resolution part as an interesting analysis. This will make the story flow a lot better. \n\n**MipNeRF**: The paper MipNeRF [1] seems to address a similar problem on training implicit representations in low/multi-resolution settings. It seems relevant in Table 2.\n\nMinor question: Hands are often very small in images when the full body is present but I am curious to know the source of this information - \"the hand occupies less than 1.5% of captured images\" (L24).\n\n- [1] Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields\n- [2] HARP: Personalized Hand Reconstruction from a Monocular RGB Video\n- [3] LiveHand: Real-time and Photorealistic Neural Hand Rendering\n- [4] Handy: Towards a high fidelity 3D hand shape and appearance model\n\n\n\n## Justification\n\nOverall, I like the problem the authors are trying to address but the writing is a bit hard to follow because the story struggles with two rather different tasks. I suggest the authors to polish on this aspect. For the experiments, the 3D hand avatar method comparison is not very comprehensive.", "was_revised": true, "final_justification": "My initial concerns were the lack of published baselines for comparison. The authors provided additional baselines during the rebuttal and have addressed my questions. I will raise my score but suggest the authors in the final paper to include the additional baselines, clarify research goal for the paper writing, and discuss social impact.", "rebuttal": "## Common Response to Reviewers\nWe sincerely thank the reviewers for their constructive feedback. We appreciate all reviewers recognizing the motivation of our work and the novelty of the method, along with the SOTA results. We have conducted several additional experiments to address remaining concerns.\n\n1. 3D reconstruction experiment with other hand reconstruction methods: HARP[R1] , LiveHand [R2]. \n\n2. 3D Consistency-considered super-resolution experiment including Gaussian splatting, SRGS [R3]. \n\n3. Extreme resolution experiment to show the lower-bound of SRHand, and the results for different occlusion rates. \n\nPlease refer to the additional experiment results for your consideration.\n\nReferences:  \n[R1] Korrawe Karunratanakul, and et al. Harp: Personalized hand reconstruction from a monocular rgb video. In CVPR, 2023.\n\n[R2] Akshay Mundra, and et al. Livehand: Real-time and photorealistic neural hand rendering. In ICCV, 2023.\n\n[R3] Xiang Feng, and et al. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024.\n\n\n**Q1: Compare with more valid baselines.**  \nA1: For 3D hand reconstruction baselines, HARP [1], LiveHand [2], and other prior works (Handy [3], HandAvatar [4]) were not included as XHand [5] and UHM [6] significantly outperform these methods. Nevertheless, we present additional experiments below for HARP and LiveHand, which are among the recent works. HARP reconstructs a 3D hand using mesh subdivision with 3,093 vertices, which is not sufficient to represent a detailed hand geometric shape (significantly less than ours: 49,281 vertices). Also, assuming a one-point light condition differs from the benchmark settings used in our experiments, and yields geometric artifacts. LiveHand employs low-resolution NeRF combined with a super-resolution network for real-time hand rendering. While efficient, LR NeRF and CNN-based SR module likely loses the 3D geometric information. Both methods encounter difficulties in reconstructing 3D hands from inconsistent super-resolved images and lack performance compared to ours.\n\n**Table 1:** Quantitative comparison of 3D reconstruction results. For fair comparison, our GIIF module was used for super-resolving images.\n| SR Module | 3D Recon. Methods | PSNR   | LPIPS   | SSIM   | P2P (*mm*) |\n|-----------|------------------|--------|---------|--------|------------|\n| GIIF      | HARP [1]         | 26.25  | 0.0872  | 0.8773 | 3.38       |\n|           | LiveHand [2]     | 26.91  | 0.0821  | 0.8805 | -          |\n|           | XHand [5]    | 27.71       | 0.0507     |  0.8876    |     3.43   |\n|           | Ours             | **29.88** | **0.0362** | **0.9206** | **2.16**    |\n\n\nMip-NeRF [7] tackles a different objective from ours; while it renders at across continuous scales, it remains bounded to the scale of training images, not performing super-resolution. Prior works of super-resolution on top of NeRF [8, 9, 10], therefore, do not directly compare with Mip-NeRF, only mentioning it as a related work.\n\nInstead, we include experiments with SRGS [11], which learns Gaussian splatting from super-resolved images for static scenes. As SRGS supports a fixed upscale factor, we arbitrarily control its factor through iterative upscaling. As shown below, our method achieves a performance comparable to other baselines designed for static scene reconstruction, while supporting dynamic and animatable hand avatars, which the other methods cannot afford to. As the upscaling factor increases, SRHand significantly outperforms the prior works, particularly in perceptual metrics such as LPIPS and SSIM. This is because PSNR measures only the mean squared pixel difference, where overly smooth or blurred textures can still yield high scores, whereas LPIPS and SSIM better capture perceptual fidelity and structural details.\n\n**Table 2:** Quantitative comparison of 3D consistency considered SR methods. NeRF-SR and DiSR-NeRF are brought from the main paper for comparisons.\n| Upscale factor | Methods     | PSNR   | LPIPS   | SSIM   |\n|----------------|-------------|--------|---------|--------|\n| ×4             | NeRF-SR     | 28.83  | 0.1410  | 0.8638 |\n|                | DiSR-NeRF   | 28.82  | 0.1303  | 0.8736 |\n|                | SRGS [11]   | **32.74** | 0.0731  | **0.9335** |\n|                | SRHand      | 29.06  | **0.0475** | 0.9016 |\n| ×8             | SRGS        | **29.22** | 0.1520  | 0.8739 |\n|                | SRHand      | 28.86  | **0.0487** | **0.9007** |\n| ×16            | SRGS        | 27.18  | 0.3872  | 0.7899 |\n|                | SRHand      | **27.52** | **0.0566** | **0.8858** |\n\n\n**Q2: Adjust the story and focus on 3d avatar creation.**  \nA2: Our paper is presently written in the order of process (SR $\\rightarrow$ 3D reconstruction $\\rightarrow$ fine-tuning). The current structure, introducing the SR module first in the method section and presenting the SR experiments before 3D results, might overemphasize the SR module. To counteract this, we will refine the narrative and adjust the section flow to make the 3D hand avatar creation the clear focal point. Specifically:\n\n*1. Method structure (Sec. 3):* We will revise the method section to introduce SRHand as a unified 3D hand avatar reconstruction framework. The SR module (GIIF) will be described as a sub-module to support SR–3D unified structure instead of treating them as sequentially independent tasks.\n\n*2. Experiment ordering (Sec. 4):*  We will reorder the experimental results to present 3D reconstruction metrics and qualitative meshes first, followed by SR evaluations as an auxiliary task. Additionally, we also plan to balance the section length to mainly focus on the 3D avatar results and discussions.\n\n*3. Introduction (Sec 1.) & Conclusion (Sec 2.):* We will revise the introduction to clearly convey that the SR module is an integral part of the 3D reconstruction pipeline, designed to enable high-fidelity mesh recovery under low-resolution, pose-varying conditions rather than serving as an independent objective. The conclusion will be updated accordingly to emphasize SR–3D perspective as the central contribution of the work.\n\n\n**Q3: Hand Occupancy Region.**  \nA3: The statement (L24) is based on our empirical observation across the full-body image datasets. We computed the pixel-wise ratio between the region using tight bounding boxes of hand and full-body in several representative samples. We present its comparisons in the table below, where a single hand occupies up to 1.5\\% of the whole depending on its pose.\n\n**Table 3:** Pixel-level hand region comparison to body region across multiple datasets. (BB stands for bounding box.)\n\n| Sample # (Dataset)           | Body BB Size | Hand BB Size | Ratio  |\n|------------------------------|--------------|--------------|--------|\n| Sample 0 (Goliath [12])      | 594 × 1590   | 102 × 141    | 1.52 % |\n| Sample 1 (PeopleSnapshot [13]) | 640 × 912   | 72 × 90      | 1.11 % |\n| Sample 2 (ActorsHQ [14])     | 341 × 836    | 45 × 84      | 1.32 % |\n\n\n**Q4: Negative Social Impact.**  \nA4: We agree that hand geometry and motion patterns can implicitly contain biometric cues. Our model is trained on the InterHand2.6M dataset, which is collected for public research purposes. Furthermore, the GIIF learns a generalized hand prior, not identity labels, and our 3D reconstruction pipeline cannot encode personal identifiers as long as they are absent in input images. This design setting minimizes privacy risk; still, some texture information related to personal identity can be implicitly learned in the GIIF network. We will clarify this social impact in the final version.\n\nReferences:  \n[1] Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, and Siyu Tang. Harp: Personalized hand reconstruction from a monocular rgb video. In CVPR, 2023.\n\n[2] Akshay Mundra, Mallikarjun B R, Jiayi Wang, Marc Habermann, Christian Theobalt, and Mohamed Elgharib. Livehand: Real-time and photorealistic neural hand rendering. In ICCV, 2023.\n\n[3] Rolandos Alexandros Potamias, Stylianos Ploumpis, Stylianos Moschoglou, Vasilios Triantafyllou, and Stefanos Zafeiriou. Handy: Towards a high fidelity 3D hand shape and appearance model. In CVPR, 2023.\n\n[4] Xingyu Chen, Baoyuan Wang, and Heung-Yeung Shum. Hand avatar: Free-pose hand animation and rendering from monocular video. In CVPR, 2023.\n\n[5] Gyeongsik Moon, Weipeng Xu, Rohan Joshi, Chenglei Wu, and Takaaki Shiratori. Authentic hand avatar from a phone scan via universal hand model. In CVPR, 2024.\n\n[6] Qijun Gan, Zijie Zhou, and Jianke Zhu. Xhand: Real-time expressive hand avatar. arXiv:2407.21002, 2024.\n\n[7] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. In CVPR, 2021.\n\n[8] Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High-quality neural radiance fields using supersampling. In ACM, 2022.\n\n[9] Xudong Huang, Wei Li, Jie Hu, Hanting Chen, and Yunhe Wang. RefSR-NeRF: Towards High Fidelity and Super Resolution View Synthesis. In CVPR, 2023.\n\n[10] Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In CVPR, 2024.\n\n[11] Xiang Feng, and et al. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024.\n\n[12] Julieta Martinez, and et al. Codec avatar studio: Paired human captures for complete, driveable, and generalizable avatars. In NeurIPS Track on Datasets and Benchmarks, 2024.\n\n[13] Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt and Gerard Pons-Moll. Video Based Reconstruction of 3D People Models. In CVPR, 2018.\n\n[14] Mustafa Işık, Martin Rünz, Markos Georgopoulos, Taras Khakhulin, Jonathan Starck, Lourdes Agapito, Matthias Nießner. HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion. In ACM TOG, 2023."}, {"review_id": "7L3MD9n9X0", "reviewer": "Reviewer_dx64", "summary": "The paper describes a framework for super-resolving hand avatars from very low resolution multi-view images. It integrates an image SR and a 3D hand reconstruction module to model high fidelity avatars. It uses an implicit image function that is conditioned on surface normals extracted from a MANO model, making it geometry aware - called Geometric-aware Implicit Image Function (GIIF).\nThe method is validated on real-world datasets, demonstrating improvements in appearance and geometry quality, such as wrinkles, nails, and shape variations.\n\nIt uses a high detailed mesh like XHand, but does a better job in disentangling the color changes due to texture vs. geometry. This is done by combining LIIF-GAN style adversarial implicit function learning (to support any resolution) with MANO based normals for geometry awareness, followed by fine tuning. On InterHand and Goliath, reported PSNR, LPIPS, and P2P metrics show very significant improvements over competitors.\n\nSignificance is a bit unclear as the paper doesn't discuss where it's getting the pose estimates, and how robust the method is to imperfect pose. With lower resolution, hand pose also degrades, but the paper seems to ignore it. The scenario where the method is applied seems very unrealistic otherwise, where we have images that barely look like hands, but perfect hand pose somehow.", "questions": "My main question is around what happens with bad or noisy initial poses. If the system is not robust at all the noisy J, it shouldn't be attempting to show results with super low resolution input, as it makes no sense. Ideally the pose should also come from the same type of LR images if this is expected to have any real world impact. \n\nI may have misunderstood the paper of course, in which case I'd be happy to increase my score.", "limitations": "Basically all my questions about robustness to pose noise apply here.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: \n- novel integration of a LIIF-GAN with explicit 3D meshes, combining the advantages of continuous, arbitrary-resolution detail generation from implicit representations with the structured geometric prior and animatability provided by explicit meshes.\n\n- fine-tuning mechanism enforces multi-view and pose consistency for highly deformable hands. \n\n- extensive quantitative and qualitative results show clear improvement over the SOTA.\n\nWeaknesses:\n- The paper seems to assume that hand pose is coming from somewhere, and it doesn't discuss what happens when the initial pose is noisy or simply bad. The input images used in results look ridiculously distorted such that no hand tracker would be able to extract any meaningful pose from them, so I think it's clear that the final pose completely depends on having a perfect pose estimate. The fine tuning stage doesn't clearly explain if pose itself is refined, but it doesn't look like it. In any case ablation study or limitations section don't mention what happens with bad poses either. My feeling is that if the paper assumes it will have perfect GT pose from 34x34 hand images somehow, it has less significance than it claims, because it can't be done. If it can actually refine the very bad initial poses, and get results that are so close to GT, then it has even more significance then it claims in the paper, but doesn't mention it.\n\n- The paper does not directly compare against SOTA that leverage 3D Gaussian Splatting or diffusion models for 3D content generation. E.g. \"3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement\" by Luo et al, which looks like it could also do a good job with hands with some adaptation.", "was_revised": true, "final_justification": "I'd like to thank the authors for the discussion. After seeing the results without the GT pose, I decided to increase my score to borderline accept.", "rebuttal": "## Common Response to Reviewers\nWe sincerely thank the reviewers for their constructive feedback. We appreciate all reviewers recognizing the motivation of our work and the novelty of the method, along with the SOTA results. We have conducted several additional experiments to address remaining concerns.\n\n1. 3D reconstruction experiment with other hand reconstruction methods: HARP[R1] , LiveHand [R2]. \n\n2. 3D Consistency-considered super-resolution experiment including Gaussian splatting, SRGS [R3]. \n\n3. Extreme resolution experiment to show the lower-bound of SRHand, and the results for different occlusion rates. \n\nPlease refer to the additional experiment results for your consideration.\n\nReferences:  \n[R1] Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, and Siyu Tang. Harp: Personalized hand reconstruction from a monocular rgb video. In CVPR, 2023.\n\n[R2] Akshay Mundra, Mallikarjun B R, Jiayi Wang, Marc Habermann, Christian Theobalt, and Mohamed Elgharib. Livehand: Real-time and photorealistic neural hand rendering. In ICCV, 2023.\n\n[R3] Xiang Feng, Yongbo He, Yan Yang, and Yubo Wang, Yifei Chen, Zhan Wang, Zhenzhong Kuang, Feiwei Qin, Jiajun Ding. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024.\n\n**Q1: Using GT Hand Pose.**  \nA1: In the main paper, we used ground-truth pose parameters to provide an upper bound and to isolate the effect of our SR–3D reconstruction pipeline. This evaluation protocol is a widely adopted strategy in prior works [1, 2, 3, 4, 5], which also leverage GT SMPL parameters to reconstruct detailed shapes from incomplete, occluded, or degraded images while decoupling pose estimation errors from reconstruction quality.\n\nTo further assess the robustness of SRHand over pose errors, we conducted experiments in the supplementary Section 3.4 by adding Gaussian noise to the GT parameters. In a multi-view video capture setup, global constraints allow pose parameters to be fitted jointly across views, reducing the joint error to *mm* scale [6, 7]. With the perturbations up to 9.54 *mm* (~1*cm*) per joint, SRHand maintained stable performance, remaining tolerant to realistic levels of pose inaccuracy. Nonetheless, extreme pose errors limit performance, as is common in most hand reconstruction pipelines.\n\nRegarding the question of the low-resolution settings and GT existness, in a multi-view body capture system, hand region resolution dynamically changes depending on its pose and camera viewpoint. Even in the same pose, one camera may capture a small region of the hand while others capture a larger region. For example, in the ActorsHQ [8] dataset, within the same pose, a single hand region varies (45×46 ~ 108×127) depending on its camera views. Thus, even though the camera captures the hand in a low resolution, global 3D pose parameters can still be extracted across multiple cameras. \n\nIn addition, considering this variability observed in real datasets, we evaluated SRHand on multiple input resolutions, including 48×48, which approximates the hand region resolution in real-world datasets (ActorsHQ in Table 3 for reviewer yhNh and Supplementary Section 3.5; see also Table 1 below). We find out that as the input image resolution increases, the reconstruction accuracy typically improves. We also include experiments with even smaller hand crops to probe the lower limit of SRHand. As noted in another review (sv5s), which requested testing at extreme small scales, SRHand maintained faithful reconstruction results without significant degradation compared to other resolution scales. Nevertheless, estimating accurate hand poses from extremely low-resolution images remains an open research challenge.\n\n**Table 1:** Quantitative results of arbitrary scale input image experiment.\n| Input image resolution | Super-Resolution PSNR | Super-Resolution LPIPS | 3D Reconstruction PSNR | 3D Reconstruction LPIPS |\n|------------------------|-----------------------|------------------------|------------------------|-------------------------|\n| 32×32                  | 31.05                 | 0.0262                 | 30.23                  | 0.0341                  |\n| 48×48                  | 31.42                 | 0.0274                 | 30.43                  | 0.0334                  |\n\n\n**Q2: More experiments for 3D content generation.**  \nA2: Certain works (e.g., 3DEnhancer [9] and LGM [10]) do not align with our research goal and are difficult to compare for several reasons. First, generating 3D objects is entirely different from reconstructing detailed personalized hand avatars from captured images. Reconstructing a personalized avatar is to optimize the model to capture finer details across input images, whether a generalizable 3D object generation task is to work upon a large-scale pretraining model (e.g MVDream [11]). None of the prior avatar reconstruction works [12, 13, 14] has been compared with the methods for 3D generic object generation tasks. Second, their architectures are specifically designed for static objects and scenes, not an animatable avatar. Their proposed modules, such as multi-view row attention, near-view epipolar aggregation, are designed for static objects and are not straightforward to extend for dynamic human avatars. Their training codes are also not publicly available. Regarding the existing diffusion-based models, IDM [15] and DiSR-NeRF [16] are both diffusion-based methods and are included as part of the comparative experiments in our main paper. \n\nInstead, we include experiments with SRGS [17], which learns Gaussian splatting with super-resolved images for static scenes. \nAs SRGS supports a fixed upscale factor, we arbitrarily control its factor through iterative upscaling. As shown below, our method achieves performance comparable to other baselines for static 3D hand reconstruction, while still supporting animatable hand avatars. Furthermore, as the upscaling factor increases, SRHand significantly outperforms the prior works, particularly in perceptual metrics such as LPIPS and SSIM. This is because PSNR measures only the mean squared pixel difference, where overly smooth or blurred textures can still yield high scores, whereas LPIPS and SSIM better capture perceptual fidelity and structural detail.\n\n\n**Table 2:** Quantitative comparison of 3D consistency considered SR methods. NeRF-SR and DiSR-NeRF are brought from the main paper for comparisons.\n| Upscale factor | Methods     | PSNR   | LPIPS   | SSIM   |\n|----------------|-------------|--------|---------|--------|\n| ×4             | NeRF-SR     | 28.83  | 0.1410  | 0.8638 |\n|                | DiSR-NeRF   | 28.82  | 0.1303  | 0.8736 |\n|                | SRGS [17]   | **32.74** | 0.0731  | **0.9335** |\n|                | SRHand      | 29.06  | **0.0475** | 0.9016 |\n| ×8             | SRGS        | **29.22** | 0.1520  | 0.8739 |\n|                | SRHand      | 28.86  | **0.0487** | **0.9007** |\n| ×16            | SRGS        | 27.18  | 0.3872  | 0.7899 |\n|                | SRHand      | **27.52** | **0.0566** | **0.8858** |\n\nReference:  \n[1] Junying Wang, Jae Shin Yoon, Tuanfeng Y. Wang, Krishna Kumar Singh, Ulrich Neumann, Complete 3D human Reconstruction from a Singe Incomplete Image. In CVPR, 2023.\n\n[2] Jihyun Lee, Minhyuk Sung, Honggyu Choi, and Tae-Kyun Kim. Im2hands: Learning attentive implicit representation of interacting two-hand shapes. In CVPR, 2023. \n\n[3] Zeren Jiang, Chen Guo, Manuel Kaufmann, Tianjian Jiang, Julien Valentin, Otmar Hilliges, Jie Song. MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild. In CVPR, 2024.\n\n[4] Minje Kim, and Tae-Kyun Kim. BiTT: Bi-directional Texture Reconstruction of Personalized Interacting Two Hands from a Single Image. In CVPR, 2024.\n\n[5] Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, and Weisi Lin. Robust-PIFU: Robust Pixel-Aligned Implicit Function for 3D Human Digitalization from A Single Image. In ICLR, 2025.\n\n[6] Wei Xie, Zhipeng Yu, Zimeng Zhao, Binghui Zuo, Yangang Wang. HMDO : Markerless Multi-view Hand Manipulation Capture with Deformable Objects, In Graphic Models, 2023.\n\n[7] Xiaozheng Zheng, Chao Wen, Zhou Xue, Pengfei Ren, Jingyu Wang1. HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning. In ICCV, 2023.\n\n[8] Mustafa Işık, Martin Rünz, Markos Georgopoulos, Taras Khakhulin, Jonathan Starck, Lourdes Agapito, Matthias Nießner. HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion. In ACM TOG, 2023.\n\n[9] Yihang Luo, Shangchen Zhou, Yushi Lan, Xingang Pan, and Chen Change Loy. 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement. arXiv:2412.18565, 2024.\n\n[10] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation. In ECCV, 2024.\n\n[11] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. MVDream: Multi-view Diffusion for 3D Generation. In ICLR, 2024.\n\n[12] Tianjian Jiang, Xu Chen, Jie Song1, and Otmar Hilliges. InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds. In CVPR, 2023.\n\n[13] Hezhen Hu, Zhiwen Fan, Tianhao Walter Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang. Expressive Gaussian Human Avatars from Monocular RGB Video. In NIPS, 2024.\n\n[14] Hsuan-I Ho, Jie Song, and Otmar Hilliges. SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion. In CVPR, 2024.\n\n[15] Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, and Baochang Zhang. Implicit diffusion models for continuous super-resolution. In CVPR, 2023.\n\n[18]  Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In CVPR, 2024.\n\n[19] Xiang Feng, Yongbo He, Yan Yang, and Yubo Wang, Yifei Chen, Zhan Wang, Zhenzhong Kuang, Feiwei Qin, Jiajun Ding. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024."}, {"review_id": "FGRdgqXMbu", "reviewer": "Reviewer_sv5s", "summary": "The paper introduces the SRHand framework, which combines geometry - aware implicit image functions with explicit 3D mesh optimization to reconstruct detailed 2D hand images and 3D shapes from low - resolution images. It shows superior performance on InterHand2.6M and Goliath datasets compared to existing methods.", "questions": "pls refer to weaknesses.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\n(1) Novelty: It's the first to merge implicit neural representations (GIIF) with explicit reconstruction mesh, ensuring multi - view/pose consistency via normal - map priors and reducing reliance on high - resolution inputs.\n\n(2) Comprehensive experiments and strong detail reconstruction: It achieves a PSNR of 31.60 under x16 super - resolution, captures fine - grained features like wrinkles and nails.\n\n(3) Open - source code and high reproducibility.\n\n(4) SRHand has great potential for practical deployment and engineering implementation.\n\n### **Weaknesses**\n\n(1) Can it show detail - reconstruction results under extremely low - resolution inputs and test the lower limit?\n\n(2) Is there validation under challenging conditions like complex occlusions?\n\n(3) Missing related references, such as I2UV - HandNet (iccv 2021), which performs super - resolution of 3D hand meshes based on UV maps.", "was_revised": true, "final_justification": "Thank you for your rebuttal—it has resolved all my concerns. I will raise my score to 5 and hope the authors will revise the paper in future versions according to the rebuttal.", "rebuttal": "## Common Response to Reviewers\nWe sincerely thank the reviewers for their constructive feedback. We appreciate all reviewers recognizing the motivation of our work and the novelty of the method, along with the SOTA results. We have conducted several additional experiments to address remaining concerns.\n\n1. 3D reconstruction experiment with other hand reconstruction methods: HARP[R1] , LiveHand [R2]. \n\n2. 3D Consistency-considered super-resolution experiment including Gaussian splatting, SRGS [R3]. \n\n3. Extreme resolution experiment to show the lower-bound of SRHand, and the results for different occlusion rates. \n\nPlease refer to the additional experiment results for your consideration.\n\nReferences:  \n[R1] Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, and Siyu Tang. Harp: Personalized hand reconstruction from a monocular rgb video. In CVPR, 2023.\n\n[R2] Akshay Mundra, Mallikarjun B R, Jiayi Wang, Marc Habermann, Christian Theobalt, and Mohamed Elgharib. Livehand: Real-time and photorealistic neural hand rendering. In ICCV, 2023.\n\n[R3] Xiang Feng, Yongbo He, Yan Yang, and Yubo Wang, Yifei Chen, Zhan Wang, Zhenzhong Kuang, Feiwei Qin, Jiajun Ding. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024.\n\n**Q1: Experiments on extremely small image resolution and complex occlusions.**  \nA1: Our main experiments, ×16 upscale factor, already target a realistic setting in both human and hand reconstruction literature, where the hand occupies 1.5% (L24) of the full body size. To further probe the lower limit of SRHand, we conducted an additional experiment with an upscaling factor of ×32 (where the input images are as small as 8×8). While this factor goes beyond typical practical scenarios, it serves as a stress test to evaluate how well the joint SR–3D pipeline preserves geometry and texture. Thanks to the strong combination of the SR module and the 3D reconstruction module, the joint optimization maintains comparable performance even under this extreme setting, as shown in the table below. Furthermore, seeing the arbitrary input resolution experiments presented in Supplementary Section 3.5 together, these results demonstrate that SRHand scales consistently: as the input image resolution increases, 3D reconstruction quality improves accordingly.\n\n\n**Table 1:** Quantitative results of the arbitrary scale input image experiment, including an extreme resolution image.\n| Upscale factor | Super-Resolution PSNR | Super-Resolution LPIPS | 3D Reconstruction PSNR | 3D Reconstruction LPIPS |\n|----------------|-----------------------|------------------------|------------------------|-------------------------|\n| ×32            | 28.82                 | 0.0341                 | 28.23                  | 0.0409                  |\n| ×16            | 30.06                 | 0.0302                 | 29.88                  | 0.0362                  |\n| ×10.6          | 30.52                 | 0.0293                 | 30.08                  | 0.0343                  |\n\n\n**Q2: Validation under complex occlusions.**  \nA2: We also present complex occlusion experiments. Hand reconstruction is well-known to be challenging due to frequent self-occlusions and highly articulated poses. Our multi-view video setup is designed to alleviate these issues, yet detailed geometric reconstruction remains difficult in occluded regions. To simulate such challenging conditions, we reduce the number of training cameras to one or two and sample pose-varying frames. The occlusion rate is quantified through the percentage of invisible vertices of the hand in the camera viewing space. As expected, the reconstruction accuracy decreases as the occlusion rate increases, shown in the table below; however, not drastically. \n\n**Table 2:** Quantitative results under diverse occlusion rate.  \n| Occlusion Rate (Train View #) | PSNR  | LPIPS  | SSIM   |\n|------------------------------|-------|--------|--------|\n| 24.7% (1)                    | 25.54 | 0.0704 | 0.8636 |\n| 19.8% (1)                    | 26.56 | 0.0597 | 0.8874 |\n| 9.43% (2)                    | 28.05 | 0.0510 | 0.8636 |\n| 0%                           | 29.88 | 0.0362 | 0.9206 |\n\n\n**Q3: Missing Reference.**  \nA3: I2UV-HandNet [1] focuses on reconstructing a high-resolution UV-map hand mesh (without texture) from a single image, which has a different objective compared to ours. Nevertheless, its combination of super-resolution and hand geometry reconstruction shares conceptual similarities with our approach, and we will include I2UV-HandNet as a related work in the final version.\n\n\nReference:  \n[1] Ping Chen, Yujin Chen, Dong Yang, Fangyin Wu, Qin Li, Qingpei Xia, Yong Tan. I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling. In ICCV, 2021."}, {"review_id": "1gRTvOvGp1", "reviewer": "Reviewer_hSnG", "summary": "This paper proposes SRHand, a novel framework for super-resolving low-resolution hand images and reconstructing high-fidelity 3D hand meshes. The method integrates a Geometric-aware Implicit Image Function (GIIF) for continuous image upsampling with an explicit high-resolution mesh-based 3D reconstruction module. Furthermore, an adaptive fine-tuning strategy is introduced to enforce multi-view and pose consistency by jointly optimizing the image super-resolution module and explicit 3D shape. Experiments on InterHand2.6M and Goliath datasets show SOTA performance both quantitatively and qualitatively, demonstrating the effectiveness in reconstructing highly detailed hand avatars from low-resolution images.", "questions": "1. Define variant “NAIIF” in Figure 2 and Figure 4. \n2. Please explain the innovation about GIIF module.", "limitations": "- Ambiguity in Terminology. The paper introduces GIIF as the main SR module. However, Figure 2 and Figure 4 labels one variant as “NAIIF”, which is not formally defined in the main text.\n- The GIIF module mainly reuses known components: normal maps as structural priors (as used in SuperNeRF-GAN [2]), and adversarial losses (as in LIIF-GAN [1]). In particular, SuperNeRF-GAN proposed to use the normal maps as geometric priors to guide the super-resolution process within a NeRF-based framework. While the integration is novel in the context of hand avatars, the module itself lacks substantial architectural innovation.\n\n[1] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, 2021.\n\n[2] Peng Zheng, Linzhi Huang, Yizhou Yu, Yi Chang, Yilin Wang, and Rui Ma. Supernerf-gan: A universal 3d-consistent super-resolution framework for efficient and enhanced 3d-aware image synthesis. arXiv.2501.06770, 2025.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "- The paper tackles the under-explored but practically important problem of reconstructing detailed hand geometry from low-resolution multi-view inputs, which is common in full-body capture pipelines.\n- The GIIF module is technically well-motivated. The use of normal maps as 3D geometric priors has been validated in several human reconstruction works, such as ECON [3], which demonstrates that normals can effectively guide surface detail recovery. Building upon this idea, the GIIF leverages normal maps from the MANO to introduce structural awareness into the image super-resolution process. \n- The proposed method significantly outperforms baseline SR and 3D reconstruction methods in PSNR, LPIPS, SSIM, and P2P metrics. Ablation studies further support the effectiveness of each component.\n\n[1] Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, and Michael J. Black. ECON: Explicit Clothed Humans Optimized via Normal Integration. In CVPR, 2023.", "was_revised": true, "final_justification": "According to the authors’ rebuttal and additional experiments, I increase my score. Thanks for the additional explanation. I hope the authors can add these content in the final version.", "rebuttal": "## Common Response to Reviewers\nWe sincerely thank the reviewers for their constructive feedback. We appreciate all reviewers recognizing the motivation of our work and the novelty of the method, along with the SOTA results. We have conducted several additional experiments to address remaining concerns.\n\n1. 3D reconstruction experiment with other hand reconstruction methods: HARP[R1] , LiveHand [R2]. \n\n2. 3D Consistency-considered super-resolution experiment including Gaussian splatting, SRGS [R3]. \n\n3. Extreme resolution experiment to show the lower-bound of SRHand, and the results for different occlusion rates. \n\nPlease refer to the additional experiment results for your consideration.\n\nReferences:  \n[R1] Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, and Siyu Tang. Harp: Personalized hand reconstruction from a monocular rgb video. In CVPR, 2023.\n\n[R2] Akshay Mundra, Mallikarjun B R, Jiayi Wang, Marc Habermann, Christian Theobalt, and Mohamed Elgharib. Livehand: Real-time and photorealistic neural hand rendering. In ICCV, 2023.\n\n[R3] Xiang Feng, Yongbo He, Yan Yang, and Yubo Wang, Yifei Chen, Zhan Wang, Zhenzhong Kuang, Feiwei Qin, Jiajun Ding. SRGS: Super-Resolution 3D Gaussian Splatting. arXiv:2404.10318, 2024.\n\n\n**Q1: Ambiguity in Terminology.**  \nA1: We had included ***'known_typo_main_figure.pdf**'* in the submitted supplementary file mentioning those typos.\n\n**Q2: Innovation about GIIF module.**    \nA2: The main contribution of SRHand lies not in GIIF alone, but in the joint optimization of the SR module and the 3D reconstruction module, which enables consistent super-resolved images and high-resolution animatable hand meshes to be learned simultaneously. GIIF supports this pipeline as GIIF key modules are used in the joint optimization and affect the 3D reconstruction stage. Integrating GIIF into a tightly coupled SR–3D pipeline for dynamic, low-resolution hand avatars, to the best of our knowledge, is a novel contribution. This differs fundamentally from prior image SR methods, which operate independently of 3D learning or induced by 3D consistency for static objects or scenes.  \n\nReplacing GIIF with LIIF (or LIIF* variants) leads to a noticeable drop in both SR quality (Tab. 1) and 3D reconstruction accuracy (Tab. 3). Importantly, the adaptive fine-tuning stage, where GIIF and the mesh module are updated together, reduces P2P error from 3.09*mm* to 2.16*mm*, highlighting that our novelty stems from the SR–3D joint optimization and module sharing across stages."}]

Author rebuttal:

Meta-review: The paper has initially received thorough reviews. The reviewers also discussed the rebuttal in depth.

The rebuttal has helped address a lot of concerns, and the reviewers agreed to revised their score.

In the current form, we recommend Accept, and encourage the authors to take into account the reviewers' comments for the final version. Congratulations!

E.g., suggestions from reviewers:
*  Include the additional baselines, clarify research goal for the paper writing, and discuss social impact.
Decision: Accept (poster)