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.

本研究提出了一种新的方法——Synthetic Pose-aware Animal ControlNet (SPAC-Net)，该方法将ControlNet整合到先前提出的Prior-Aware Synthetic animal data generation (PASyn)管道中，使用变分自编码器（VAE）生成的合理姿势数据来训练高精度姿势估计网络，从而生成更接近真实数据的具有姿势标签的合成数据，并通过SPAC-Net管道进行测试，证明了合成数据克服动物姿态估计中有限注释数据的潜力。

SPAC-Net: 增强姿态估计的动物姿态感知合成网络