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3D reconstruction from 2D observations is a highly challenging area in computer vision, primarily due to ambiguities such as scale, depth, occlusions, etc. My work leverages deep learning techniques for 3D reconstruction, incorporating parametric human models to enhance estimation accuracy. To address the challenges posed by this inherently ill-posed problem, various constraints and regularizations are applied. The focus is on the domain of sign language, where capturing intricate articulations and detailed facial expressions is crucial. The pipeline involves detecting features in 2D, reconstructing the 3D model, and rendering it back into the image space.