180 research outputs found
Optical Flow in Mostly Rigid Scenes
The optical flow of natural scenes is a combination of the motion of the
observer and the independent motion of objects. Existing algorithms typically
focus on either recovering motion and structure under the assumption of a
purely static world or optical flow for general unconstrained scenes. We
combine these approaches in an optical flow algorithm that estimates an
explicit segmentation of moving objects from appearance and physical
constraints. In static regions we take advantage of strong constraints to
jointly estimate the camera motion and the 3D structure of the scene over
multiple frames. This allows us to also regularize the structure instead of the
motion. Our formulation uses a Plane+Parallax framework, which works even under
small baselines, and reduces the motion estimation to a one-dimensional search
problem, resulting in more accurate estimation. In moving regions the flow is
treated as unconstrained, and computed with an existing optical flow method.
The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art
results on both the MPI-Sintel and KITTI-2015 benchmarks.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201
Model-based Optical Flow: Layers, Learning, and Geometry
The estimation of motion in video sequences establishes temporal correspondences between pixels and surfaces and allows reasoning about a scene using multiple frames. Despite being a focus of research for over three decades, computing motion, or optical flow, remains challenging due to a number of difficulties, including the treatment of motion discontinuities and occluded regions, and the integration of information from more than two frames. One reason for these issues is that most optical flow algorithms only reason about the motion of pixels on the image plane, while not taking the image formation pipeline or the 3D structure of the world into account. One approach to address this uses layered models, which represent the occlusion structure of a scene and provide an approximation to the geometry. The goal of this dissertation is to show ways to inject additional knowledge about the scene into layered methods, making them more robust, faster, and more accurate.
First, this thesis demonstrates the modeling power of layers using the example of motion blur in videos, which is caused by fast motion relative to the exposure time of the camera. Layers segment the scene into regions that move coherently while preserving their occlusion relationships. The motion of each layer therefore directly determines its motion blur. At the same time, the layered model captures complex blur overlap effects at motion discontinuities. Using layers, we can thus formulate a generative model for blurred video sequences, and use this model to simultaneously deblur a video and compute accurate optical flow for highly dynamic scenes containing motion blur.
Next, we consider the representation of the motion within layers. Since, in a layered model, important motion discontinuities are captured by the segmentation into layers, the flow within each layer varies smoothly and can be approximated using a low dimensional subspace. We show how this subspace can be learned from training data using principal component analysis (PCA), and that flow estimation using this subspace is computationally efficient. The combination of the layered model and the low-dimensional subspace gives the best of both worlds, sharp motion discontinuities from the layers and computational efficiency from the subspace.
Lastly, we show how layered methods can be dramatically improved using simple semantics. Instead of treating all layers equally, a semantic segmentation divides the scene into its static parts and moving objects. Static parts of the scene constitute a large majority of what is shown in typical video sequences; yet, in such regions optical flow is fully constrained by the depth structure of the scene and the camera motion. After segmenting out moving objects, we consider only static regions, and explicitly reason about the structure of the scene and the camera motion, yielding much better optical flow estimates. Furthermore, computing the structure of the scene allows to better combine information from multiple frames, resulting in high accuracies even in occluded regions. For moving regions, we compute the flow using a generic optical flow method, and combine it with the flow computed for the static regions to obtain a full optical flow field.
By combining layered models of the scene with reasoning about the dynamic behavior of the real, three-dimensional world, the methods presented herein push the envelope of optical flow computation in terms of robustness, speed, and accuracy, giving state-of-the-art results on benchmarks and pointing to important future research directions for the estimation of motion in natural scenes
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in
low-level vision: single view depth prediction, camera motion estimation,
optical flow, and segmentation of a video into the static scene and moving
regions. Our key insight is that these four fundamental vision problems are
coupled through geometric constraints. Consequently, learning to solve them
together simplifies the problem because the solutions can reinforce each other.
We go beyond previous work by exploiting geometry more explicitly and
segmenting the scene into static and moving regions. To that end, we introduce
Competitive Collaboration, a framework that facilitates the coordinated
training of multiple specialized neural networks to solve complex problems.
Competitive Collaboration works much like expectation-maximization, but with
neural networks that act as both competitors to explain pixels that correspond
to static or moving regions, and as collaborators through a moderator that
assigns pixels to be either static or independently moving. Our novel method
integrates all these problems in a common framework and simultaneously reasons
about the segmentation of the scene into moving objects and the static
background, the camera motion, depth of the static scene structure, and the
optical flow of moving objects. Our model is trained without any supervision
and achieves state-of-the-art performance among joint unsupervised methods on
all sub-problems.Comment: CVPR 201
Semantic Photo Manipulation with a Generative Image Prior
Despite the recent success of GANs in synthesizing images conditioned on
inputs such as a user sketch, text, or semantic labels, manipulating the
high-level attributes of an existing natural photograph with GANs is
challenging for two reasons. First, it is hard for GANs to precisely reproduce
an input image. Second, after manipulation, the newly synthesized pixels often
do not fit the original image. In this paper, we address these issues by
adapting the image prior learned by GANs to image statistics of an individual
image. Our method can accurately reconstruct the input image and synthesize new
content, consistent with the appearance of the input image. We demonstrate our
interactive system on several semantic image editing tasks, including
synthesizing new objects consistent with background, removing unwanted objects,
and changing the appearance of an object. Quantitative and qualitative
comparisons against several existing methods demonstrate the effectiveness of
our method.Comment: SIGGRAPH 201
Seeing What a GAN Cannot Generate
Despite the success of Generative Adversarial Networks (GANs), mode collapse
remains a serious issue during GAN training. To date, little work has focused
on understanding and quantifying which modes have been dropped by a model. In
this work, we visualize mode collapse at both the distribution level and the
instance level. First, we deploy a semantic segmentation network to compare the
distribution of segmented objects in the generated images with the target
distribution in the training set. Differences in statistics reveal object
classes that are omitted by a GAN. Second, given the identified omitted object
classes, we visualize the GAN's omissions directly. In particular, we compare
specific differences between individual photos and their approximate inversions
by a GAN. To this end, we relax the problem of inversion and solve the
tractable problem of inverting a GAN layer instead of the entire generator.
Finally, we use this framework to analyze several recent GANs trained on
multiple datasets and identify their typical failure cases.Comment: ICCV 2019 oral; http://ganseeing.csail.mit.edu
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