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Indirect deep structured learning for 3D human body shape and pose prediction
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 (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
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
A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation
Statistical body shape models are widely used in 3D pose estimation due to
their low-dimensional parameters representation. However, it is difficult to
avoid self-intersection between body parts accurately. Motivated by this fact,
we proposed a novel self-intersection penalty term for statistical body shape
models applied in 3D pose estimation. To avoid the trouble of computing
self-intersection for complex surfaces like the body meshes, the gradient of
our proposed self-intersection penalty term is manually derived from the
perspective of geometry. First, the self-intersection penalty term is defined
as the volume of the self-intersection region. To calculate the partial
derivatives with respect to the coordinates of the vertices, we employed
detection rays to divide vertices of statistical body shape models into
different groups depending on whether the vertex is in the region of
self-intersection. Second, the partial derivatives could be easily derived by
the normal vectors of neighboring triangles of the vertices. Finally, this
penalty term could be applied in gradient-based optimization algorithms to
remove the self-intersection of triangular meshes without using any
approximation. Qualitative and quantitative evaluations were conducted to
demonstrate the effectiveness and generality of our proposed method compared
with previous approaches. The experimental results show that our proposed
penalty term can avoid self-intersection to exclude unreasonable predictions
and improves the accuracy of 3D pose estimation indirectly. Further more, the
proposed method could be employed universally in triangular mesh based 3D
reconstruction
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural Body Fitting (NBF)). It integrates a statistical body model
within a CNN, leveraging reliable bottom-up semantic body part segmentation and
robust top-down body model constraints. NBF is fully differentiable and can be
trained using 2D and 3D annotations. In detailed experiments, we analyze how
the components of our model affect performance, especially the use of part
segmentations as an explicit intermediate representation, and present a robust,
efficiently trainable framework for 3D human pose estimation from 2D images
with competitive results on standard benchmarks. Code will be made available at
http://github.com/mohomran/neural_body_fittingComment: 3DV 201