6,356 research outputs found
Learning Dense 3D Models from Monocular Video
Reconstructing dense, detailed, 3D shape of dynamic scenes from monocular sequences is a challenging problem in computer vision. While robust and even real-time solutions exist to this problem if the observed scene is static, for non-rigid dense shape capture current systems are typically restricted to the use of complex multi-camera rigs, taking advantage of the additional depth channel available in RGB-D cameras, or dealing with specific shapes such as faces or planar surfaces. In this thesis, we present two pieces of work for reconstructing dense generic shapes from monocular sequences. In the first work, we propose an unsupervised approach to the challenging problem of simultaneously segmenting the scene into its constituent objects and reconstructing a 3D model of the scene. The strength of our approach comes from the ability to deal with real-world dynamic scenes and to handle seamlessly different types of motion: rigid, articulated and non-rigid. We formulate the problem as a hierarchical graph-cuts based segmentation where we decompose the whole scene into background and foreground objects and model the complex motion of non-rigid or articulated objects as a set of overlapping rigid parts. To validate the capability of our approach to deal with real-world scenes, we provide 3D reconstructions of some challenging videos from the YouTube Objects and KITTI dataset, etc. In the second work, we propose a direct approach for capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single camera. Our method makes use of a single RGB video as input; it can capture the deformations of generic shapes; and the depth estimation is dense, per-pixel and direct. We first reconstruct a dense 3D template of the shape of the object, using a short rigid sequence, and subsequently perform online reconstruction of the non-rigid mesh as it evolves over time. In our experimental evaluation, we show a range of qualitative results on novel datasets and quantitative comparison results with stereo reconstruction
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
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CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 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
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Learning to predict scene depth from RGB inputs is a challenging task both
for indoor and outdoor robot navigation. In this work we address unsupervised
learning of scene depth and robot ego-motion where supervision is provided by
monocular videos, as cameras are the cheapest, least restrictive and most
ubiquitous sensor for robotics.
Previous work in unsupervised image-to-depth learning has established strong
baselines in the domain. We propose a novel approach which produces higher
quality results, is able to model moving objects and is shown to transfer
across data domains, e.g. from outdoors to indoor scenes. The main idea is to
introduce geometric structure in the learning process, by modeling the scene
and the individual objects; camera ego-motion and object motions are learned
from monocular videos as input. Furthermore an online refinement method is
introduced to adapt learning on the fly to unknown domains.
The proposed approach outperforms all state-of-the-art approaches, including
those that handle motion e.g. through learned flow. Our results are comparable
in quality to the ones which used stereo as supervision and significantly
improve depth prediction on scenes and datasets which contain a lot of object
motion. The approach is of practical relevance, as it allows transfer across
environments, by transferring models trained on data collected for robot
navigation in urban scenes to indoor navigation settings. The code associated
with this paper can be found at https://sites.google.com/view/struct2depth.Comment: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI'19
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