Despite that 3D human body reconstruction from a single image has obtained rapid progress in recent years, most methods aim at the body without the hands and face. However, hand gestures and facial expressions are also important for delivering human intentions or emotions. This paper proposes a method for holistic 3D reconstruction of the human body from a single RGB image, including hands, body, and face. Our approach is based on the SMPL eXpressive (SMPL-X), a unified 3D parametric human body model of body, hands, and face. Since it is difficult to exactly regress the model's parameters of different body parts by a single framework, we use a divide-and-conquer strategy for the whole human body reconstruction. We exploit different deep neural networks to predict the hand, body, and head model's parameters, then integrate them into an entire 3D model to realize a holistic and expressive 3D human body reconstruction. Simulation results demonstrate that our method has obtained state-of-the-art performance with better facial expression.
3D reconstruction from a single RGB image is making some progress especially with the advent of deep learning in recent years. In this paper, we focus on 3D reconstruction of indoor scenes taking as input a single image, without point clouds, multi-view images, depth or masks. Our mesh reconstruction is based on an encoder-decoder framework of mesh deformation. Features extracted by the encoder from the input image will be used as supervision for mesh deformation. We propose an inter-point interaction attention in the decoder to exploit the vertexes’ influence on each other. We also use a smooth loss to generate smoother surface for objects. We have evaluated the proposed framework on the Pix3D dataset, and state-of-the-art performance has been achieved with visually appealing 3D geometry.
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