3,050 research outputs found
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13
page
It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data
We address the problem of 3D human pose estimation from 2D input images using
only weakly supervised training data. Despite showing considerable success for
2D pose estimation, the application of supervised machine learning to 3D pose
estimation in real world images is currently hampered by the lack of varied
training images with corresponding 3D poses. Most existing 3D pose estimation
algorithms train on data that has either been collected in carefully controlled
studio settings or has been generated synthetically. Instead, we take a
different approach, and propose a 3D human pose estimation algorithm that only
requires relative estimates of depth at training time. Such training signal,
although noisy, can be easily collected from crowd annotators, and is of
sufficient quality for enabling successful training and evaluation of 3D pose
algorithms. Our results are competitive with fully supervised regression based
approaches on the Human3.6M dataset, despite using significantly weaker
training data. Our proposed algorithm opens the door to using existing
widespread 2D datasets for 3D pose estimation by allowing fine-tuning with
noisy relative constraints, resulting in more accurate 3D poses.Comment: BMVC 2018. Project page available at
http://www.vision.caltech.edu/~mronchi/projects/RelativePos
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
- …