Kavli Affiliate: Feng Wang
| First 5 Authors: Daiheng Gao, Yuliang Xiu, Kailin Li, Lixin Yang, Feng Wang
| Summary:
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 dart2022.github.io
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