18,466 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
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
Forecasting Human Dynamics from Static Images
This paper presents the first study on forecasting human dynamics from static
images. The problem is to input a single RGB image and generate a sequence of
upcoming human body poses in 3D. To address the problem, we propose the 3D Pose
Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on
single-image human pose estimation and sequence prediction, and converts the 2D
predictions into 3D space. We train our 3D-PFNet using a three-step training
strategy to leverage a diverse source of training data, including image and
video based human pose datasets and 3D motion capture (MoCap) data. We
demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and
3D pose recovery through quantitative and qualitative results.Comment: Accepted in CVPR 201
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior. Despite progress on
single-image 3D pose and shape estimation, existing video-based
state-of-the-art methods fail to produce accurate and natural motion sequences
due to a lack of ground-truth 3D motion data for training. To address this
problem, we propose Video Inference for Body Pose and Shape Estimation (VIBE),
which makes use of an existing large-scale motion capture dataset (AMASS)
together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty
is an adversarial learning framework that leverages AMASS to discriminate
between real human motions and those produced by our temporal pose and shape
regression networks. We define a temporal network architecture and show that
adversarial training, at the sequence level, produces kinematically plausible
motion sequences without in-the-wild ground-truth 3D labels. We perform
extensive experimentation to analyze the importance of motion and demonstrate
the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving
state-of-the-art performance. Code and pretrained models are available at
https://github.com/mkocabas/VIBE.Comment: CVPR-2020 camera ready. Code is available at
https://github.com/mkocabas/VIB
- …