4,894 research outputs found
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
We present a method for simultaneously estimating 3D human pose and body
shape from a sparse set of wide-baseline camera views. We train a symmetric
convolutional autoencoder with a dual loss that enforces learning of a latent
representation that encodes skeletal joint positions, and at the same time
learns a deep representation of volumetric body shape. We harness the latter to
up-scale input volumetric data by a factor of , whilst recovering a
3D estimate of joint positions with equal or greater accuracy than the state of
the art. Inference runs in real-time (25 fps) and has the potential for passive
human behaviour monitoring where there is a requirement for high fidelity
estimation of human body shape and pose
BodyNet: Volumetric Inference of 3D Human Body Shapes
Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. 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.
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. Each of them
results in performance improvement as demonstrated by our experiments. 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. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018).
27 page
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
3D object reconstruction is a fundamental task of many robotics and AI
problems. With the aid of deep convolutional neural networks (CNNs), 3D object
reconstruction has witnessed a significant progress in recent years. However,
possibly due to the prohibitively high dimension of the 3D object space, the
results from deep CNNs are often prone to missing some shape details. In this
paper, we present an approach which aims to preserve more shape details and
improve the reconstruction quality. The key idea of our method is to leverage
object mask and pose estimation from CNNs to assist the 3D shape learning by
constructing a probabilistic single-view visual hull inside of the network. Our
method works by first predicting a coarse shape as well as the object pose and
silhouette using CNNs, followed by a novel 3D refinement CNN which refines the
coarse shapes using the constructed probabilistic visual hulls. Experiment on
both synthetic data and real images show that embedding a single-view visual
hull for shape refinement can significantly improve the reconstruction quality
by recovering more shapes details and improving shape consistency with the
input image.Comment: 11 page
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