用低分辨率手部图像重建高细节3D手形与纹理，保持多视角一致性。
SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Neural Image Representations and Explicit 3D Meshes
- 结合隐式图像函数与显式网格，学习手部几何先验并上采样
- 在InterHand2.6M和Goliath数据集上显著优于现有方法
- 适合需要高精度手部建模的VR/AR、动作捕捉应用
重建精细手部形象在诸多应用中至关重要。以往方法依赖高分辨率多视角输入，难以泛化到低分辨率图像。虽已有基于多视角超分的方法，但仅适用于静态物体且不适用于可变形的手部。本文提出SRHand，从低分辨率图像重建高细节3D手形及带纹理的图像。该方法结合隐式图像表示与显式手部网格，引入几何感知隐式图像函数（GIIF），通过上采样粗略输入图像学习手部先验。联合优化隐式图像函数与显式3D手形，保持上采后图像的多视角与姿态一致性，实现精细重建（如皱纹、指甲）。在InterHand2.6M与Goliath数据集上的实验表明，本方法在定量与定性上均显著优于现有图像超分与3D手部重建方法。
原文摘要 · Abstract (English)
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. Project page: https://yunminjin2.github.io/projects/srhand
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