  Estimating the articulated 3D hand-object pose from a single RGB image is a
highly ambiguous and challenging problem, requiring large-scale datasets that
contain diverse hand poses, object types, and camera viewpoints. Most
real-world datasets lack these diversities. In contrast, data synthesis can
easily ensure those diversities separately. However, constructing both valid
and diverse hand-object interactions and efficiently learning from the vast
synthetic data is still challenging. To address the above issues, we propose
ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover
diverse hand-object poses and camera viewpoints through sampling in a
Composited hand-object Configuration and Viewpoint space (CCV-space) and can
adaptively enrich the current hard-discernable items by loss-feedback and
sample re-weighting. ArtiBoost alternatively performs data exploration and
synthesis within a learning pipeline, and those synthetic data are blended into
real-world source data for training. We apply ArtiBoost on a simple learning
baseline network and witness the performance boost on several hand-object
benchmarks. Our models and code are available at
https://github.com/lixiny/ArtiBoost.
