4/29/2023 0 Comments Ufish silhouette![]() In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-the-wild". Besides achieving state-of-the-art performance, our method also enables volumetric body-part segmentation.ģD models provide the common ground for different representations of human bodies. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Each of them results in performance improvement as demonstrated by our experiments. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Human shape estimation is an important task for video editing, animation and fashion industry. We show how a proposed technique for estimating uncertainty in deep learning models can be applied to give uncertainty estimates for our 3d pose predictions. Finally despite the prevalence of deep learning on computer vision tasks the approach suffers from a significant limitation: it produces predictions that are point-estimates without any associated measure of uncertainty surrounding the prediction. We also show how the model can be fine-tuned with additional signals that use more widely available 2D information. We use ground-truth available from the synthetic data to train a regression model that directly predicts the parameters of the body model. We use a large synthetic dataset to address the issue of providing enough data for deep learning. This richer representation allows us to make use of additional signals to resolve some of the ambiguities in going from 2D to 3D. Unlike many solutions that use just a skeleton to represent the 3D pose we utilise a body model that provides shape/volume as well as pose. We use a deep learning approach to this problem addressing the challenges in a number ways. This is a further challenge to the problem of 3D pose estimation since obtaining human annotations of 3D data is far more difficult and time-consuming than obtaining the equivalent labels for 2D data. However deep learning's success is predicated on the availability of large quantities of labeled training data. There are many possible solutions so how do we select the right one? Deep learning has become the state-of-the-art for many problems in computer vision. The problem is fundamentally ill-posed as it involves inverting the imaging process to arrive at the 3D points that generated the 2D image. We address the challenging problem of estimating 3D human pose from a single monocular image.
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