860 research outputs found
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
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
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations
Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this
paper, we present a geometry-driven approach to automatically collect
annotations for human pose prediction tasks. Starting from a generic ConvNet
for 2D human pose, and assuming a multi-view setup, we describe an automatic
way to collect accurate 3D human pose annotations. We capitalize on constraints
offered by the 3D geometry of the camera setup and the 3D structure of the
human body to probabilistically combine per view 2D ConvNet predictions into a
globally optimal 3D pose. This 3D pose is used as the basis for harvesting
annotations. The benefit of the annotations produced automatically with our
approach is demonstrated in two challenging settings: (i) fine-tuning a generic
ConvNet-based 2D pose predictor to capture the discriminative aspects of a
subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from
scratch for single view 3D human pose prediction without leveraging 3D pose
groundtruth. The proposed multi-view pose estimator achieves state-of-the-art
results on standard benchmarks, demonstrating the effectiveness of our method
in exploiting the available multi-view information.Comment: CVPR 2017 Camera Read
Exploiting temporal information for 3D pose estimation
In this work, we address the problem of 3D human pose estimation from a
sequence of 2D human poses. Although the recent success of deep networks has
led many state-of-the-art methods for 3D pose estimation to train deep networks
end-to-end to predict from images directly, the top-performing approaches have
shown the effectiveness of dividing the task of 3D pose estimation into two
steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from
images and then mapping them into 3D space. They also showed that a
low-dimensional representation like 2D locations of a set of joints can be
discriminative enough to estimate 3D pose with high accuracy. However,
estimation of 3D pose for individual frames leads to temporally incoherent
estimates due to independent error in each frame causing jitter. Therefore, in
this work we utilize the temporal information across a sequence of 2D joint
locations to estimate a sequence of 3D poses. We designed a
sequence-to-sequence network composed of layer-normalized LSTM units with
shortcut connections connecting the input to the output on the decoder side and
imposed temporal smoothness constraint during training. We found that the
knowledge of temporal consistency improves the best reported result on
Human3.6M dataset by approximately and helps our network to recover
temporally consistent 3D poses over a sequence of images even when the 2D pose
detector fails
- …