We present Implicit Two Hands (Im2Hands), the first neural implicit representation of two interacting hands. Unlike existing methods on two-hand reconstruction that rely on a parametric hand model and/or low-resolution meshes, Im2Hands can produce fine-grained geometry of two hands with high hand-to-hand and hand-to-image coherency. To handle the shape complexity and interaction context between two hands, Im2Hands models the occupancy volume of two hands - conditioned on an RGB image and coarse 3D keypoints - by two novel attention-based modules responsible for (1) initial occupancy estimation and (2) context-aware occupancy refinement, respectively. Im2Hands first learns per-hand neural articulated occupancy in the canonical space designed for each hand using query-image attention. It then refines the initial two-hand occupancy in the posed space to enhance the coherency between the two hand shapes using query-anchor attention. In addition, we introduce an optional keypoint refinement module to enable robust two-hand shape estimation from predicted hand keypoints in a single-image reconstruction scenario. We experimentally demonstrate the effectiveness of Im2Hands on two-hand reconstruction in comparison to related methods, where ours achieves state-of-the-art results. Our code is publicly available at https://github.com/jyunlee/Im2Hands.

翻译：我们提出了隐式双手模型（Im2Hands），这是首个对两只交互手进行神经隐式表征的方法。与现有依赖参数化手部模型和/或低分辨率网格的双手重建方法不同，Im2Hands能够生成具有高手-手一致性与手-图像一致性的精细双手几何结构。为应对双手间的形状复杂性和交互上下文，Im2Hands以RGB图像和粗粒度3D关键点为条件，通过两个新颖的基于注意力的模块对双手占据体积进行建模，这两个模块分别负责：(1)初始占据估计和(2)上下文感知的占据精化。Im2Hands首先在针对每只手设计的规范空间中，利用查询-图像注意力学习每只手的神经关节占据。随后在姿态空间中精化初始双手占据，通过查询-锚点注意力增强两只手形状间的一致性。此外，我们引入了一个可选的关键点精化模块，以实现在单张图像重建场景中基于预测手部关键点的鲁棒双手形状估计。通过与相关方法的对比实验，我们证明了Im2Hands在双手重建任务中的有效性，该方法达到了最先进的结果。我们的代码已公开于https://github.com/jyunlee/Im2Hands。