Hand, the bearer of human productivity and intelligence, is receiving much
attention due to the recent fever of digital twins. Among different hand
morphable models, MANO has been widely used in vision and graphics community.
However, MANO disregards textures and accessories, which largely limits its
power to synthesize photorealistic hand data. In this paper, we extend MANO
with Diverse Accessories and Rich Textures, namely DART. DART is composed of 50
daily 3D accessories which varies in appearance and shape, and 325 hand-crafted
2D texture maps covers different kinds of blemishes or make-ups. Unity GUI is
also provided to generate synthetic hand data with user-defined settings, e.g.,
pose, camera, background, lighting, textures, and accessories. Finally, we
release DARTset, which contains large-scale (800K), high-fidelity synthetic
hand images, paired with perfect-aligned 3D labels. Experiments demonstrate its
superiority in diversity. As a complement to existing hand datasets, DARTset
boosts the generalization in both hand pose estimation and mesh recovery tasks.
Raw ingredients (textures, accessories), Unity GUI, source code and DARTset are
publicly available at this http URL

本文介绍了一种针对手部建模的 MANO 模型的扩展版 --DART，其中包含了 50 个不同款式和形状的 3D 配饰和 325 幅手工制作的 2D 纹理贴图。通过 Unity GUI，用户可以生成具有多样化姿态、摄像机、背景、灯光、纹理和配饰的合成手数据，并发布了一份包含 800K 高度逼真合成手影像的数据集 DARTset，用于增加手姿估计和网格恢复任务的泛化能力。

具有丰富纹理和多种配件的关节手模型 DART

DART: Articulated Hand Model with Diverse Accessories and Rich Textures

We introduce and evaluate several architectures for Convolutional Neural
Networks to predict the 3D joint locations of a hand given a depth map. We
first show that a prior on the 3D pose can be easily introduced and
significantly improves the accuracy and reliability of the predictions. We also
show how to use context efficiently to deal with ambiguities between fingers.
These two contributions allow us to significantly outperform the
state-of-the-art on several challenging benchmarks, both in terms of accuracy
and computation times.

本文研究使用卷积神经网络结构，预测手的三维关节位置，并通过引入关节先验和有效利用上下文来提高方案的预测精确性和可靠性，在多个基准测试中都表现出了远胜于现有技术的高精度和较快的计算时间。