  We propose a new large synthetic hand pose estimation dataset, Hi5, and a
novel inexpensive method for collecting high-quality synthetic data that
requires no human annotation or validation. Leveraging recent advancements in
computer graphics, high-fidelity 3D hand models with diverse genders and skin
colors, and dynamic environments and camera movements, our data synthesis
pipeline allows precise control over data diversity and representation,
ensuring robust and fair model training. We generate a dataset with 583,000
images with accurate pose annotation using a single consumer PC that closely
represents real-world variability. Pose estimation models trained with Hi5
perform competitively on real-hand benchmarks while surpassing models trained
with real data when tested on occlusions and perturbations. Our experiments
show promising results for synthetic data as a viable solution for data
representation problems in real datasets. Overall, this paper provides a
promising new approach to synthetic data creation and annotation that can
reduce costs and increase the diversity and quality of data for hand pose
estimation.
