Indirect deep structured learning for 3D human body shape and pose prediction
نویسندگان
چکیده
In this paper we present a novel method for 3D human body shape and pose prediction. Our work is motivated by the need to reduce our reliance on costly-to-obtain ground truth labels. To achieve this, we propose training an encoder-decoder network using a two step procedure as follows. During the first step, a decoder is trained to predict a body silhouette using SMPL [2] (a statistical body shape model) parameters as an input. During the second step, the whole network is trained on real image and corresponding silhouette pairs while the decoder is kept fixed. Such a procedure allows for an indirect learning of body shape and pose parameters from real images without requiring any ground truth parameter data. Our key contributions include: (a) a novel encoder-decoder architecture for 3D body shape and pose prediction, (b) corresponding training procedure as well as (c) quantitative and qualitative analysis of the proposed method on artificial and real image datasets.
منابع مشابه
Structured Prediction of 3D Human Pose with Deep Neural Networks
Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce a Deep Lea...
متن کاملV2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D dept...
متن کامل3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network
In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: 1) a multi-task framework that jointly trains pose regression and body part detectors; 2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large dat...
متن کاملSelf Adversarial Training for Human Pose Estimation
This paper presents a deep learning based approach to the problem of human pose estimation. We employ generative adversarial networks as our learning paradigm in which we set up two stacked hourglass networks with the same architecture, one as the generator and the other as the discriminator. The generator is used as a human pose estimator after the training is done. The discriminator distingui...
متن کاملShape Models of the Human Body for Distributed Inference
of “Shape Models of the Human Body for Distributed Inference” by Silvia Zuffi, Ph.D., Brown University, May 2015 In this thesis we address the problem of building shape models of the human body, in 2D and 3D, which are realistic and efficient to use. We focus our efforts on the human body, which is highly articulated and has interesting shape variations, but the approaches we present here can b...
متن کامل