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.
Given low-resolution hand images, our method firstly reconstructs high-resolution hand images using GIIF. Then, using these images, we reconstruct detailed 3D shapes while jointly optimizing the GIIF through adaptive fine-tuning from 3D shapes.
Below 3D hand models are reconstructed from low-resolution 16 x 16 hand images. Its images are super-resolved to 256 x 256 resolution by our GIIF. Click and drag to rotate the view, scroll to zoom in/out, and right-click and drag to pan around the model. 3D shapes.
Quantitative comparisons between compared methods using InterHand2.6M and Goliath 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 in red, orange, and yellow, respectively.
Our proposed GIIF is a geometric-aware implicit image function that learns detailed hand prior by upsampling the coarse input images. Given any resolution of input images, our GIIF can reconstruct high-resolution images with fine details.
(a) Given LR images, we reconstruct high-resolution 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.
@InProceedings{kim2025srhand,
author = {Kim, Minje and Kim, Tae-Kyun},
title = {SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Nueral Image Representations and Explicit 3D Meshes},
booktitle = {Advances in Neural Information Processing Systems (NIPS)},
year = {2025}
}
This work was in part supported by