Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE’s expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud.

Recent speech-based services such as voice assistants and cloud computing services have brought security concerns, since those services constantly send user's speech data to the server. Speech data contain user's sensitive biometric data and spoken words and can be misused if it is leaked into wrong hands. In this context, Signal Processing in Encrypted Domain (SPED) can be a solution by mixing up Homomorphic Encryption (HE) with signal processing. Using HE enables computing on user's encrypted data without decrypting it, thus providing security and privacy. In this paper, we present a simple, but fast homomorphic Quantized Fourier Transform (QFT) with efficient packing of speech signals. Our work is based on the Fully Homomor-phic Encryption (FHE) scheme TFHE, which was proposed by Chillotti et al. We then present a thorough noise analysis of our QFT that helps to keep a reasonable noise level. Also, considering the TFHE's bootstrapping manner, we statistically analyze the boundary of the QFT coefficients, and present a simple criterion for scaling up the coefficients. Our criteria help keep the message precision as high as possible during the TFHE bootstrapping. We use our criteria to evaluate the magnitude of QFT with low latency, but with reasonable precision. Finally, we provide a proof-of-concept implementation of our QFT. With a ring dimension of 1024 and TFHE parameters that achieve 106 bits of security, we show that the QFT can be evaluated in 35 milliseconds for a single ciphertext of length 1024. This result is 74.6 times faster than in the previous work. We also built a homomorphic end-to-end speech processing framework that processes and classifies gender (resp. vowel) of encrypted speech data from the VoxCeleb (resp. PCVC) dataset. Our implementation classifies the gender (resp. vowel) with more than 83% (resp. 79%) accuracy with a minimum of 0.05 (resp. 0.56) seconds with multithreading.

Abstract Direct manipulation can be used as an intuitive interface for spatial interaction with multiple objects in virtual environments. However, users often have difficulties in precisely manipulating objects in the 3D space; this is mainly because the user is not provided with enough visual information to sufficiently recognize the spatial relationships among objects. We present a new method for improving the preciseness of object manipulation in the 3D space by providing the user with an auxiliary view that is consistently retargeted to closely highlight the spatial relationship between the manipulated object and the object nearest to it. Both the target and the position of the auxiliary camera are computed efficiently using the bisector surface of the two involved objects. The viewing target is set to the center of the closest area between the two objects, and the camera position is optimized under an objective function primarily measuring the visual clarity of the auxiliary view. We demonstrate the usefulness of our method through various experiments based on practical usage scenarios.

Abstract We present a points‐based user interface for character posing. In our method, users insert a number of three‐dimensional (3D) points in a virtual environment. The system performs a linear search of a motion capture database for the best matched pose and then places the pose immediately in the virtual environment to be overlapped with the input points. For a fast and precise distance computation between the input points and the example poses from the database, we developed a closed‐form solution of the 3D points registration problem. To demonstrate the easiness and usability of our approach, we built a motion database including various kinds of human motion and conducted a user study of character posing tasks with non‐expert users. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract We present a method for the interactive control of big‐object manipulation animation. When a human character holds a big arbitrarily shaped object, it should use the whole upper body, including both arms and the torso in a limited range such that the holding postures can be physically as well as visually believable to users. In our method, we first sample various postures that satisfy our feasibility conditions: collision free and physically stable. The concept of the wrench space is used to evaluate the physical stability of holding postures. Then, we build a graph structure in which the nodes are the sampled postures and the links are collision‐free motion interpolating between two‐sampled postures. This manipulation motion graph allows us to use a general pathfinding method, such as Dijkstra's algorithm, to achieve real‐time controllability. In our experiments, we build the manipulation motion graphs for differently shaped objects, and we demonstrate the usability of our method via several examples in which users interactively control the animation through the target positions or desired trajectories of the objects. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract We present an interactive method for creating animation sequences of characters based on captured motion data in an exploratory way as in assembling construction toys. The key component of our method is a path browser that can retrieve and visualize paths as diverse as possible connecting a given pair of initial and final motion fragments instantiated in the space. With the aid of our path browser, the user can develop large‐scale assembly of motions gradually through iterations of arranging and putting together motion fragments. For the efficient retrieval of connecting paths, we use a bidirectional search tree that grows from the initial and final configurations simultaneously under the guidance of a mixed strategy for both global exploration and local optimization. The usefulness of our approach is demonstrated through experiments with a variety of motion data including box moving, basketball, and breakdance data. Copyright © 2016 John Wiley & Sons, Ltd.

ABSTRACT We present a new approach to interactive resequencing of captured motion data that allows the user to progressively extend his or her animations such that each extension can best fit user requirements with respect to space, time, and pose. In the preprocessing phase, captured frames are clustered into pose groups and interconnected into a transition graph. For each step in building an animation sequence at runtime, the user is allowed to browse through motion paths in the graph that are connectable to the existing sequence by specifying the desired locations and poses at path ends and to select one of those paths to extend the animation. Instead of using graph search algorithms, our path browser unrolls the graph locally into spatial search trees and employs an efficient nearest‐neighbor search method to quickly find the optimal paths that satisfy user inputs. We demonstrate the usefulness of our approach by creating animation examples in scenarios involving spatial, temporal, and postural requirements simultaneously. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract We present a new approach to motion rearrangement that preserves the syntactic structures of an input motion automatically by learning a context‐free grammar from the motion data. For grammatical analysis, we reduce an input motion into a string of terminal symbols by segmenting the motion into a series of subsequences, and then associating a group of similar subsequences with the same symbol. To obtain the most repetitive and precise set of terminals, we search for an optimial segmentation such that a large number of subsequences can be clustered into groups with little error. Once the input motion has been encoded as a string, a grammar induction algorithm is employed to build up a context‐free grammar so that the grammar can reconstruct the original string accurately as well as generate novel strings sharing their syntactic structures with the original string. Given any new strings from the learned grammar, it is straightforward to synthesize motion sequences by replacing each terminal symbol with its associated motion segment, and stitching every motion segment sequentially. We demonstrate the usefulness and flexibility of our approach by learning grammars from a large diversity of human motions, and reproducing their syntactic structures in new motion sequences.

Crowd simulation has been an important research field due to its diverse range of applications that include film production, military simulation, and urban planning. A challenging problem is to provide simple yet effective control over captured and simulated crowds to synthesize intended group motions. We present a new method that blends existing crowd data to generate a new crowd animation. The new animation can include an arbitrary number of agents, extends for an arbitrary duration, and yields a natural-looking mixture of the input crowd data. The main benefit of this approach is to create new spatio-temporal crowd behavior in an intuitive and predictable manner. It is accomplished by introducing a morphable crowd model that allows us to encode the formations and individual trajectories in crowd data. Then, its original spatio-temporal behavior can be reconstructed and interpolated at an arbitrary scale using our morphable model.

It is a hard problem to understand the search process of particle swarm optimization over high-dimensional domain. The visualization depicts the total search process and then it will allow better understanding of how to tune the algorithm. For the investigation, we adopt Sammon's mapping, which is a well-known distance-preserving mapping. We demonstrate the usefulness of the proposed methodology by applying it to some function optimization problems.

Abstract Given a pair of keyframe formations for a group consisting of multiple individuals, we present a spectral‐based approach to smoothly transforming a source group formation into a target formation while respecting the clusters of the involved individuals. The proposed method provides an effective means for controlling the macroscopic spatiotemporal arrangement of individuals for applications such as expressive formations in mass performances and tactical formations in team sports. Our main idea is to formulate this problem as rotation interpolation of the eigenbases for the Laplacian matrices, each of which represents how the individuals are clustered in a given keyframe formation. A stream of time‐varying formations is controlled by editing the underlying adjacency relationships among individuals as well as their spatial positions at each keyframe, and interpolating the keyframe formations while producing plausible collective behaviors over a period of time. An interactive system of editing existing group behaviors in a hierarchical fashion has been implemented to provide flexible formation control of large crowds.

Animating a crowd of characters is an important problem in computer graphics. The latest techniques enable highly realistic group motions to be produced in feature animation films and video games. However, interactive methods have not emerged yet for editing the existing group motion of multiple characters. We present an approach to editing group motion as a whole while maintaining its neighborhood formation and individual moving trajectories in the original animation as much as possible. The user can deform a group motion by pinning or dragging individuals. Multiple group motions can be stitched or merged to form a longer or larger group motion while avoiding collisions. These editing operations rely on a novel graph structure, in which vertices represent positions of individuals at specific frames and edges encode neighborhood formations and moving trajectories. We employ a shape-manipulation technique to minimize the distortion of relative arrangements among adjacent vertices while editing the graph structure. The usefulness and flexibility of our approach is demonstrated through examples in which the user creates and edits complex crowd animations interactively using a collection of group motion clips.

Real time animation of human figures in virtual environments is an important problem in the context of computer games and virtual environments. Recently, the use of large collections of captured motion data has increased realism in character animation. However, assuming that the virtual environment is large and complex, the effort of capturing motion data in a physical environment and adapting them to an extended virtual environment is the bottleneck for achieving interactive character animation and control. We present a new technique for allowing our animated characters to navigate through a large virtual environment, which is constructed using a set of building blocks. The building blocks, called motion patches , can be arbitrarily assembled to create novel environments. Each patch is annotated with motion data, which informs what actions are available for animated characters within the block. The versatility and flexibility of our approach are demonstrated through examples in which multiple characters are animated and controlled at interactive rates in large, complex virtual environments.