Cross-species pose estimation plays a vital role in studying neural mechanisms and behavioral patterns while serving as a fundamental tool for behavior monitoring and prediction. However, conventional image-based approaches face substantial limitations, including excessive storage requirements, high transmission bandwidth demands, and massive computational costs. To address these challenges, we introduce an image-free pose estimation framework based on single-pixel cameras operating at ultra-low sampling rates ($6.260\times 10^{-4}$). Our method eliminates the need for explicit or implicit image reconstruction, instead directly extracting pose information from highly compressed single-pixel measurements. It dramatically reduces data storage and transmission requirements while maintaining accuracy comparable to traditional image-based methods. Our solution provides a practical approach for real-world applications where bandwidth and computational resources are constrained.