5,789 research outputs found
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
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
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.Comment: Published in CVPR 201
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
We propose a new single-shot method for multi-person 3D pose estimation in
general scenes from a monocular RGB camera. Our approach uses novel
occlusion-robust pose-maps (ORPM) which enable full body pose inference even
under strong partial occlusions by other people and objects in the scene. ORPM
outputs a fixed number of maps which encode the 3D joint locations of all
people in the scene. Body part associations allow us to infer 3D pose for an
arbitrary number of people without explicit bounding box prediction. To train
our approach we introduce MuCo-3DHP, the first large scale training data set
showing real images of sophisticated multi-person interactions and occlusions.
We synthesize a large corpus of multi-person images by compositing images of
individual people (with ground truth from mutli-view performance capture). We
evaluate our method on our new challenging 3D annotated multi-person test set
MuPoTs-3D where we achieve state-of-the-art performance. To further stimulate
research in multi-person 3D pose estimation, we will make our new datasets, and
associated code publicly available for research purposes.Comment: International Conference on 3D Vision (3DV), 201
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