  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
