  In recent years, a plethora of diverse methods have been proposed for 3D pose
estimation. Among these, self-attention mechanisms and graph convolutions have
both been proven to be effective and practical methods. Recognizing the
strengths of those two techniques, we have developed a novel Semantic Graph
Attention Network which can benefit from the ability of self-attention to
capture global context, while also utilizing the graph convolutions to handle
the local connectivity and structural constraints of the skeleton. We also
design a Body Part Decoder that assists in extracting and refining the
information related to specific segments of the body. Furthermore, our approach
incorporates Distance Information, enhancing our model's capability to
comprehend and accurately predict spatial relationships. Finally, we introduce
a Geometry Loss who makes a critical constraint on the structural skeleton of
the body, ensuring that the model's predictions adhere to the natural limits of
human posture. The experimental results validate the effectiveness of our
approach, demonstrating that every element within the system is essential for
improving pose estimation outcomes. With comparison to state-of-the-art, the
proposed work not only meets but exceeds the existing benchmarks.
