This paper presents a novel approach for 3D human pose estimation from a single image, addressing the challenge of estimating full-body 3D pose using end-to-end learning. The proposed method introduces a volumetric representation of 3D pose, where the space around the subject is discretized into voxels, and a ConvNet predicts per-voxel likelihoods for each joint. This approach provides a more natural and effective representation for 3D pose estimation compared to direct regression of joint coordinates. Additionally, a coarse-to-fine prediction scheme is introduced to handle the increased dimensionality of the volumetric representation, enabling iterative refinement and repeated processing of image features. The proposed method outperforms existing state-of-the-art approaches on standard benchmarks, achieving a relative error reduction of over 30% on average. The volumetric representation is also shown to be useful in a related architecture where end-to-end training is not feasible, allowing for training with images that lack 3D groundtruth. The method is evaluated on several standard benchmarks, including Human3.6M, HumanEva-I, and KTH Football II, demonstrating its effectiveness in both controlled and real-world scenarios. The results show that the proposed approach achieves state-of-the-art performance, with significant improvements over existing methods in terms of accuracy and efficiency. The paper also discusses the practical benefits of the volumetric representation, particularly in scenarios where end-to-end training is not possible. The approach is implemented using a fully convolutional network with an hourglass design, and the results are validated through extensive quantitative and qualitative evaluations. The method is shown to be effective in both 2D and 3D pose estimation, with the volumetric representation providing a richer output that is amenable to post-processing. The paper concludes that the proposed approach provides a significant improvement in 3D human pose estimation from a single image, demonstrating the effectiveness of end-to-end learning in this challenging task.This paper presents a novel approach for 3D human pose estimation from a single image, addressing the challenge of estimating full-body 3D pose using end-to-end learning. The proposed method introduces a volumetric representation of 3D pose, where the space around the subject is discretized into voxels, and a ConvNet predicts per-voxel likelihoods for each joint. This approach provides a more natural and effective representation for 3D pose estimation compared to direct regression of joint coordinates. Additionally, a coarse-to-fine prediction scheme is introduced to handle the increased dimensionality of the volumetric representation, enabling iterative refinement and repeated processing of image features. The proposed method outperforms existing state-of-the-art approaches on standard benchmarks, achieving a relative error reduction of over 30% on average. The volumetric representation is also shown to be useful in a related architecture where end-to-end training is not feasible, allowing for training with images that lack 3D groundtruth. The method is evaluated on several standard benchmarks, including Human3.6M, HumanEva-I, and KTH Football II, demonstrating its effectiveness in both controlled and real-world scenarios. The results show that the proposed approach achieves state-of-the-art performance, with significant improvements over existing methods in terms of accuracy and efficiency. The paper also discusses the practical benefits of the volumetric representation, particularly in scenarios where end-to-end training is not possible. The approach is implemented using a fully convolutional network with an hourglass design, and the results are validated through extensive quantitative and qualitative evaluations. The method is shown to be effective in both 2D and 3D pose estimation, with the volumetric representation providing a richer output that is amenable to post-processing. The paper concludes that the proposed approach provides a significant improvement in 3D human pose estimation from a single image, demonstrating the effectiveness of end-to-end learning in this challenging task.