Articulated Pose Estimation Using Hierarchical Exemplar-Based Models

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. Specifically, we obtain more expressive spatial models by assuming independence between exemplars at different levels in the hierarchy; we also obtain stronger spatial constraints by inferring the spatial relations between parts at the same level. As our method strikes a good balance between expressiveness and strength of spatial models, it is both effective and generalizable, achieving state-of-the-art results on different benchmarks: Leeds Sports Dataset and CUB-200-2011.Comment: 8 pages, 6 figure

Stochastic Dynamics for Video Infilling

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short interval between every two frames and increase the temporal resolution. Our task, namely video infilling, however, aims to infill long intervals with plausible frame sequences. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation module to guarantee the spatial-temporal coherence among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear frame sequences with varying contents. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the given start frame to the terminal frame. Our project site is https://xharlie.github.io/projects/project_sites/SDVI/video_results.htmlComment: Winter Conference on Applications of Computer Vision (WACV 2020

A Theoretical Analysis of the Conditions for Unambiguous Node Localization in Sensor Networks

In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring distances or bearings to their neighbors. Distance information is the separation between two nodes connected by a sensing/communication link. Bearing is the angle between a sensing/communication link and the x-axis of a node's local coordinate system. We construct grounded graphs to model network localization and apply graph rigidity theory and parallel drawings to test the conditions for unique localizability and to construct uniquely localizable networks. We further investigate partially localizable networks

A First Order Analysis of Lighting, Shading, and Shadows

The shading in a scene depends on a combination of many factors---how the lighting varies spatially across a surface, how it varies along different directions, the geometric curvature and reflectance properties of objects, and the locations of soft shadows. In this paper, we conduct a complete first order or gradient analysis of lighting, shading and shadows, showing how each factor separately contributes to scene appearance, and when it is important. Gradients are well suited for analyzing the intricate combination of appearance effects, since each gradient term corresponds directly to variation in a specific factor. First, we show how the spatial {\em and} directional gradients of the light field change, as light interacts with curved objects. This extends the recent frequency analysis of Durand et al.\ to gradients, and has many advantages for operations, like bump-mapping, that are difficult to analyze in the Fourier domain. Second, we consider the individual terms responsible for shading gradients, such as lighting variation, convolution with the surface BRDF, and the object's curvature. This analysis indicates the relative importance of various terms, and shows precisely how they combine in shading. As one practical application, our theoretical framework can be used to adaptively sample images in high-gradient regions for efficient rendering. Third, we understand the effects of soft shadows, computing accurate visibility gradients. We generalize previous work to arbitrary curved occluders, and develop a local framework that is easy to integrate with conventional ray-tracing methods. Our visibility gradients can be directly used in practical gradient interpolation methods for efficient rendering

A Theoretical Analysis of the Conditions for Unambiguous Node Localization in Sensor Networks

In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring distances or bearings to their neighbors. Distance information is the separation between two nodes connected by a sensing/communication link. Bearing is the angle between a sensing/communication link and the x-axis of a node's local coordinate system. We construct grounded graphs to model network localization and apply graph rigidity theory and parallel drawings to test the conditions for unique localizability and to construct uniquely localizable networks. We further investigate partially localizable networks

A Bayesian Treatment of the Stereo Correspondence Problem Using Half-Occluded Regions

A half-occluded region in a stereo pair is a set of pixels in one image representing points in space visible to that camera or eye only, and not to the other. These occur typically as parts of the background immediately to the left and right sides of nearby occluding objects, and are present in most natural scenes. Previous approaches to stereo either ignored these unmatchable points or attempted to weed them out in a second pass. An algorithm that incorporates them from the start as a strong clue to depth discontinuities is presented. The authors first derive a measure for goodness of fit and a prior based on a simplified model of objects in space, which leads to an energy functional depending both on the depth as measured from a central cyclopean eye and on the regions of points occluded from the left and right eye perspectives. They minimize this using dynamic programming along epipolar lines followed by annealing in both dimensions. Experiments indicate that this method is very effective even in difficult scenesMathematic

Information Structures to Secure Control of Rigid Formations with Leader-Follower Structure

This paper is concerned with rigid formations of mobile autonomous agents using a leader-follower structure. A formation is a group of agents moving in real 2- or 3- dimensional space. A formation is called rigid if the distance between each pair of agents does not change over time under ideal conditions. Sensing/communication links between agents are used to maintain a rigid formation. Two agents connected by a sensing/communication link are called neighbors. There are two types of neighbor relations in rigid formations. In the first type, the neighbor relation is symmetric. In the second type, the neighbor relation is asymmetric. Rigid formations with a leader-follower structure have the asymmetric neighbor relation. A framework to analyze rigid formations with symmetric neighbor relations is given in our previous work. This paper suggests an approach to analyze rigid formations that have a leader-follower structure

Time-Varying Textures

Essentially all computer graphics rendering assumes that the reflectance and texture of surfaces is a static phenomenon. Yet, there is an abundance of materials in nature whose appearance varies dramatically with time, such as cracking paint, growing grass, or ripening banana skins. In this paper, we take a significant step towards addressing this problem, investigating a new class of time-varying textures. We make three contributions. First, we describe the carefully controlled acquisition of datasets of a variety of natural processes including the growth of grass, the accumulation of snow, and the oxidation of copper. Second, we show how to adapt quilting-based methods to time-varying texture synthesis, addressing the important challenges of maintaining temporal coherence, efficient synthesis on large time-varying datasets, and reducing visual artifacts specific to time-varying textures. Finally, we show how simple procedural techniques can be used to control the evolution of the results, such as allowing for a faster growth of grass in well lit (as opposed to shadowed) areas