Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes, some recent work has tackled the reconstruction of hand textures on top of shapes. However, these methods are often limited to capturing pixels on the visible side of a hand, requiring diverse views of the hand in a video or multiple images as input. In this paper, we propose a novel method, BiTT(Bi-directional Texture reconstruction of Two hands), which is the first end-to-end trainable method for relightable, pose-free texture reconstruction of two interacting hands taking only a single RGB image, by three novel components: 1) bi-directional (left $\leftrightarrow$ right) texture reconstruction using the texture symmetry of left / right hands, 2) utilizing a texture parametric model for hand texture recovery, and 3) the overall coarse-to-fine stage pipeline for reconstructing personalized texture of two interacting hands. BiTT first estimates the scene light condition and albedo image from an input image, then reconstructs the texture of both hands through the texture parametric model and bi-directional texture reconstructor. In experiments using InterHand2.6M and RGB2Hands datasets, our method significantly outperforms state-of-the-art hand texture reconstruction methods quantitatively and qualitatively. The code is available at https://github.com/yunminjin2/BiTT

翻译：创建个性化手部虚拟形象对于在AR/VR平台上为用户提供逼真体验至关重要。尽管以往研究大多聚焦于3D手部形状重建，近期部分工作已着手处理基于形状的手部纹理重建。然而，这些方法通常局限于采集手部可见侧的像素信息，需要以视频或多张图像形式输入手部的多视角图像。本文提出一种新颖方法BiTT（双手双向纹理重建），这是首个端到端可训练的、仅需单张RGB图像即可实现可重光照、无姿态约束的交互双手纹理重建方法，其核心创新包含三部分：1）利用左右手纹理对称性实现双向（左↔右）纹理重建；2）采用纹理参数模型进行手部纹理恢复；3）构建从粗到细的级联流水线以重建交互双手的个性化纹理。BiTT首先从输入图像中估计场景光照条件和反照率图像，进而通过纹理参数模型和双向纹理重建器恢复双手纹理。在InterHand2.6M和RGB2Hands数据集上的实验表明，本方法在定量和定性指标上均显著优于当前最先进的手部纹理重建方法。代码已开源至https://github.com/yunminjin2/BiTT