Title: SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Neural Image Representations and Explicit Meshes
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Reconstruction

Multi-view Images

Super-resolution

Consistency
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\n1. Define variant “NAIIF” in Figure 2 and Figure 4. \n2. Please explain the innovation about GIIF module.\n\nLIMITATIONS:\n- 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-16"}

comments: 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.

STRENGTHS AND WEAKNESSES:
### **Strengths**

(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.

(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.

(3) Open - source code and high reproducibility.

(4) SRHand has great potential for practical deployment and engineering implementation.

### **Weaknesses**

(1) Can it show detail - reconstruction results under extremely low - resolution inputs and test the lower limit?

(2) Is there validation under challenging conditions like complex occlusions?

(3) Missing related references, such as I2UV - HandNet (iccv 2021), which performs super - resolution of 3D hand meshes based on UV maps.

QUESTIONS:
pls refer to weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-23

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\nMy 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.\n\nLIMITATIONS:\nBasically all my questions about robustness to pose noise apply here.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n- Compare with more valid baselines in 3d avatar creation\n- In the final version, adjust the story and focus on 3d avatar creation.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}