This paper presents a simple yet effective baseline for 3D human pose estimation, focusing on the task of predicting 3D joint positions from 2D joint locations. The authors aim to understand the sources of error in state-of-the-art deep end-to-end systems and find that "lifting" ground truth 2D joint locations to 3D space can be achieved with a low error rate using a relatively simple deep feed-forward network. This network outperforms the best reported results by about 30% on the Human3.6M dataset, the largest publicly available 3D pose estimation benchmark. The system is trained on the output of a state-of-the-art 2D detector, and the results suggest that a significant portion of the error in modern deep 3D pose estimation systems stems from their visual analysis. The paper also provides a high-performance, lightweight, and easy-to-reproduce baseline, which sets a new standard for future work in this area. The authors discuss the implications of their results and suggest directions for further advancements, including the integration of visual evidence and the exploration of more complex network architectures.This paper presents a simple yet effective baseline for 3D human pose estimation, focusing on the task of predicting 3D joint positions from 2D joint locations. The authors aim to understand the sources of error in state-of-the-art deep end-to-end systems and find that "lifting" ground truth 2D joint locations to 3D space can be achieved with a low error rate using a relatively simple deep feed-forward network. This network outperforms the best reported results by about 30% on the Human3.6M dataset, the largest publicly available 3D pose estimation benchmark. The system is trained on the output of a state-of-the-art 2D detector, and the results suggest that a significant portion of the error in modern deep 3D pose estimation systems stems from their visual analysis. The paper also provides a high-performance, lightweight, and easy-to-reproduce baseline, which sets a new standard for future work in this area. The authors discuss the implications of their results and suggest directions for further advancements, including the integration of visual evidence and the exploration of more complex network architectures.