  Animal pose estimation has become a crucial area of research, but the
scarcity of annotated data is a significant challenge in developing accurate
models. Synthetic data has emerged as a promising alternative, but it
frequently exhibits domain discrepancies with real data. Style transfer
algorithms have been proposed to address this issue, but they suffer from
insufficient spatial correspondence, leading to the loss of label information.
In this work, we present a new approach called Synthetic Pose-aware Animal
ControlNet (SPAC-Net), which incorporates ControlNet into the previously
proposed Prior-Aware Synthetic animal data generation (PASyn) pipeline. We
leverage the plausible pose data generated by the Variational Auto-Encoder
(VAE)-based data generation pipeline as input for the ControlNet
Holistically-nested Edge Detection (HED) boundary task model to generate
synthetic data with pose labels that are closer to real data, making it
possible to train a high-precision pose estimation network without the need for
real data. In addition, we propose the Bi-ControlNet structure to separately
detect the HED boundary of animals and backgrounds, improving the precision and
stability of the generated data. Using the SPAC-Net pipeline, we generate
synthetic zebra and rhino images and test them on the AP10K real dataset,
demonstrating superior performance compared to using only real images or
synthetic data generated by other methods. Our work demonstrates the potential
for synthetic data to overcome the challenge of limited annotated data in
animal pose estimation.
