Abstract

Replacing overexposed or dull skies in outdoor photographs is a desirable photo manipulation. It is often necessary to color correct the foreground after replacement to make it consistent with the new sky. Methods have been proposed to automate the process of sky replacement and color correction. However, many times a color correction is unwanted by the artist or may produce unrealistic results. We propose a data-driven approach to sky-replacement that avoids color correction by finding a diverse set of skies that are consistent in color and natural illumination with the query image foreground. Our database consists of ∼ 1200 natural images spanning many outdoor categories. Given a query image, we retrieve the most consistent images from the database according to L2 similarity in feature space and produce candidate composites. The candidates are re-ranked based on realism and diversity. We used pre-trained CNN features and a rich set of hand-crafted features that encode color statistics, structural layout, and natural illumination statistics, but observed color statistics to be the most effective for this task. We share our findings on feature selection and show qualitative results and a user-study based evaluation to show the effectiveness of the proposed method.

Abstract

Human body tracking typically requires specialized capture set-ups. Although pose tracking is available in consumer devices like Microsoft Kinect, it is restricted to stick figures visualizing body part detection. In this paper, we propose a method for full 3D human body shape and motion capture of arbitrary movements from the depth channel of a single Kinect, when the subject wears casual clothes. We do not use the RGB channel or an initialization procedure that requires the subject to move around in front of the camera. This makes our method applicable for arbitrary clothing textures and lighting environments, with minimal subject intervention. Our method consists of 3D surface feature detection and articulated motion tracking, which is regularized by a statistical human body model [26]. We also propose the idea of a Consensus Mesh (CMesh) which is the 3D template of a person created from a single view point. We demonstrate tracking results on challenging poses and argue that using CMesh along with statistical body models can improve tracking accuracies. Quantitative evaluation of our dense body tracking shows that our method has very little drift which is improved by the usage of CMesh.

Abstract

Human actions comprise of joint motion of articulated body parts or “gestures”. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.

Abstract

A light field is a 4D function which captures all the radiance information of a scene. Image-based creation of light fields reconstructs the 4D space using pre-captured imagery from various views and employs refocusing to generate output images. Handheld cameras can also capture light fields using a microlens array between the sensor and the main lens, but physical constraints limit their spatial and angular resolutions. In this paper, we present a GPU based synthetic light field rendering framework that is robust and physically accurate. We demonstrate the equivalence of the standard light field camera representation with light slab representation for synthetic light fields and exhibit the capability of our framework to trace light fields of resolutions much higher than available in commercial plenoptic cameras. The light slab is rich in quality but bulky to store. Our system provides parameters to balance the quality and storage requirements. We also present a compact representation of the 4D light slabs using a video compression codec and demonstrate different quality-size combinations using these representations.

Abstract

Focus, depth-of-field, and defocus are important ele-ments that portray the aesthetic emphasis in a good pho-tograph. The ability to manipulate the focus after capture provides useful creative control to photographers. Captur-ing focal stacks - multiple images with small change in focus setting - of static scenes is relatively easy with modern cam-eras. We propose a compact representation for focal stacks using an all-in-focus image, a focal-slice index map and pair-wise defocus blur parameters. Using our representation, we show reconstruction of images with different focus effects including extended focus, multiple focus, and scene synthesis with natural focus effects. A user study shows high acceptability of the synthesized images compared to real ones. The compact and powerful representation of focal stacks makes them suitable for handling by image editing tools in order to provide flexible focus manipulation

Abstract

Depth from focus is a highly accessible method to estimate the 3D structure of everyday scenes. Today’s DSLR and mobile cameras facilitate the easy capture of multiple focused images of a scene. Focus measures (FMs) that estimate the amount of focus at each pixel form the basis of depth-from-focus methods. Several FMs have been proposed in the past and new ones will emerge in the future, each with their own strengths. We estimate a weighted combination of standard FMs that outperforms others on a wide range of scene types. The resulting composite focus measure consists of FMs that are in consensus with one another but not in chorus. Our two-stage pipeline first estimates fine depth at each pixel using the composite focus measure. A cost-volume propagation step then assigns depths from confident pixels to others. We can generate high quality depth maps using just the top five FMs from our composite focus measure. This is a positive step towards depth estimation of everyday scenes with no special equipment.

Abstract

Smartphones have become the de-facto capture devices for everyday photography. Unlike traditional digital cameras, smartphones are versatile devices with auxiliary sensors, processing power, and networking capabilities. In this work, we harness the communication capabilities of smartphones and present a synchronous/co-ordinated multi-camera capture system. Synchronous capture is important for many image/video fusion and 3D reconstruction applications. The proposed system provides an inexpensive and effective means to capture multi-camera media for such applications. Our coordinated capture system is based on a wireless protocol that uses NTP based synchronization and device specific lag compensation. It achieves sub-frame synchronization across all participating smartphones of even heterogeneous make and model. We propose a new method based on fiducial markers displayed on an LCD screen to temporally calibrate smartphone cameras. We demonstrate the utility and versatility of this system to enhance traditional videography and to create novel visual representations such as panoramic videos, HDR videos, multi-view 3D reconstruction, multi-flash imaging, and multi-camera social media.

Abstract

View-graph is an essential input to large-scale structure from motion (SfM) pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on the input viewgraph. Inconsistent or inaccurate edges can lead to inferior or wrong reconstruction. Most SfM methods remove ‘undesirable’ images and pairs using several, fixed heuristic criteria, and propose tailor-made solutions to achieve specific reconstruction objectives such as efficiency, accuracy, or disambiguation. In contrast to these disparate solutions, we propose a single optimization framework that can be used to achieve these different reconstruction objectives with task-specific cost modeling. We also construct a very efficient network-flow based formulation for its approximate solution. The abstraction brought on by this selection mechanism separates the challenges specific to datasets and reconstruction objectives from the standard SfM pipeline and improves its generalization. This paper demonstrates the application of the proposed view-graph framework with standard SfM pipeline for two particular use-cases, (i) accurate and ghost-free reconstructions of highly ambiguous datasets using costs based on disambiguation priors, and (ii) accurate and efficient reconstruction of large-scale Internet datasets using costs based on commonly used priors.

Abstract

Several methods have been proposed for large-scale 3D reconstruction from large, unorganized image collections. A large reconstruction problem is typically divided into multiple components which are reconstructed independently using structure from motion (SFM) and later merged together. Incremental SFM methods are most popular for the basic structure recovery of a single component. They are robust and effective but strictly sequential in nature. We present a multistage approach for SFM reconstruction of a single component that breaks the sequential nature of the incremental SFM methods. Our approach begins with quickly building a coarse 3D model using only a fraction of features from given images. The coarse model is then enriched by localizing remaining images and matching and triangulating remaining features in subsequent stages. The geometric information available in form of the coarse model allows us to make these stages effective, efficient, and highly parallel. We show that our method produces similar quality models as compared to standard SFM methods while being notably fast and parallel.

Abstract

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilize semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlabled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilize a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.