We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art.
翻译:我们从一个 RGB 图像中提出一个新的3D 手形重建框架,并优化手向目标的抓住。 手向目标接触区域的代表性对于准确的重建至关重要。 我们不象以前的工作那样以紫外线协调地图的形式提出密集的表示,而是以紫外线协调地图的形式提出密集的表示。 此外, 我们引入了推论时间优化, 以微调手与目标之间的握力, 并改进手与目标之间的互动。 我们的管道提高了手向目标重建的精确度, 并产生了充满活力的手纹。 在Ho3D、FreiHAND和DexYCB等数据集上的实验显示, 我们提出的方法超过了最新技术。