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Weiwei Xu

Publications and source records attributed to Weiwei Xu.

At least 109 records · Page 6Linked to original sources
arXiv2021

Attention-guided Temporally Coherent Video Object Matting

This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal aggregation module that maximizes image matting networks' strength for video matting networks. This module computes temporal correlations for pixels adjacent to each other along the time axis in feature space, which is robust against motion noises. We also design a novel loss term to train the attention weights, which drastically boosts the video matting performance. Besides, we show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network with a sparse set of user-annotated keyframes. To facilitate video matting and trimap generation networks' training, we construct a large-scale video matting dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes. Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion. Our code and dataset can be found at: https://github.com/yunkezhang/TCVOM

cs.CV↗
arXiv2021

Example-based Real-time Clothing Synthesis for Virtual Agents

We present a real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. %and therefore it is more general. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. Moreover, our example database is more representative and can be generated in parallel, and thereby saves the training time. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment compared to a conventional linear blend skinning method.

cs.GR↗
arXiv2020

AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms

Simulation systems have become an essential component in the development and validation of autonomous driving technologies. The prevailing state-of-the-art approach for simulation is to use game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (e.g., the assets for simulation) remains a manual task that can be costly and time-consuming. In addition, the fidelity of CG images still lacks the richness and authenticity of real-world images and using these images for training leads to degraded performance. In this paper we present a novel approach to address these issues: Augmented Autonomous Driving Simulation (AADS). Our formulation augments real-world pictures with a simulated traffic flow to create photo-realistic simulation images and renderings. More specifically, we use LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generate highly plausible traffic flows for cars and pedestrians and compose them into the background. The composite images can be re-synthesized with different viewpoints and sensor models. The resulting images are photo-realistic, fully annotated, and ready for end-to-end training and testing of autonomous driving systems from perception to planning. We explain our system design and validate our algorithms with a number of autonomous driving tasks from detection to segmentation and predictions. Compared to traditional approaches, our method offers unmatched scalability and realism. Scalability is particularly important for AD simulation and we believe the complexity and diversity of the real world cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility in a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.

cs.CV↗
arXiv2020

Ram pressure stripping of HI-rich galaxies infalling into massive clusters

We estimate the strength of ram pressure stripping (RPS) for HI-rich galaxies in X-ray detected clusters. We find that galaxies under stronger RPS tend to show more significantly reduced total HI mass and enhanced central SFR, compared to control galaxies in the field which have similar stellar mass, stellar surface density and integral star formation rate. Galaxies under strong or weak RPS account for around 40% of the HI-rich population at R200, and even beyond R200 in the most massive clusters. Our results imply the important role of RPS as a channel of environmental processing far before the galaxies reach the core region of clusters.

astro-ph.GA↗
arXiv2020

Quasi-Newton Solver for Robust Non-Rigid Registration

Imperfect data (noise, outliers and partial overlap) and high degrees of freedom make non-rigid registration a classical challenging problem in computer vision. Existing methods typically adopt the $\ell_{p}$ type robust estimator to regularize the fitting and smoothness, and the proximal operator is used to solve the resulting non-smooth problem. However, the slow convergence of these algorithms limits its wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization, which can handle outliers and partial overlaps. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlap, with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at https://github.com/Juyong/Fast_RNRR.

cs.CV↗
arXiv2020

Singular Vectors From Singular Values

In the recent paper \cite{1}, Denton et al. provided the eigenvector-eigenvalue identity for Hermitian matrices, and a survey was also given for such identity in the literature. The main aim of this paper is to present the identity related to singular vectors and singular values of a general matrix.

math.NA↗
arXiv2019

AutoRemover: Automatic Object Removal for Autonomous Driving Videos

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over $19\%$.

cs.CV↗
arXiv2019

Computational Design of Skinned Quad-Robots

We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins.Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory.We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype.

cs.GR↗
arXiv2018

A New X-ray Selected Sample of Very Extended Galaxy Groups from the ROSAT All-Sky Survey

Some indications for tension have long been identified between cosmological constraints obtained from galaxy clusters and primary CMB measurements. Typically, assuming the matter density and fluctuations, as parameterized with Omega_m and sigma_8, estimated from CMB measurements, many more clusters are expected than those actually observed. One possible explanation could be that certain types of galaxy groups or clusters were missed in samples constructed in previous surveys, resulting in a higher incompleteness than estimated. We aim to determine if a hypothetical class of very extended, low surface brightness, galaxy groups or clusters have been missed in previous X-ray cluster surveys based on the ROSAT All-Sky Survey (RASS). We applied a dedicated source detection algorithm sensitive also to more unusual group or cluster surface brightness distributions. We found many known but also a number of new group candidates, which are not included in any previous X-ray / SZ cluster catalogs. In this paper, we present a pilot sample of 13 very extended groups discovered in the RASS at positions where no X-ray source has been detected previously and with clear optical counterparts. The X-ray fluxes of at least 5 of these are above the nominal flux-limits of previous RASS cluster catalogs. They have low mass ($10^{13} - 10^{14} M_{\odot}$; i.e., galaxy groups), are at low redshift (z<0.08), and exhibit flatter surface brightness distributions than usual. We demonstrate that galaxy groups were missed in previous RASS surveys, possibly due to the flat surface brightness distributions of this potential new population. Analysis of the full sample will show if this might have a significant effect on previous cosmological parameter constraints based on RASS cluster surveys. (This is a shortened version of the abstract - full text in the article)

astro-ph.CO↗
arXiv2018

DeepWarp: DNN-based Nonlinear Deformation

DeepWarp is an efficient and highly re-usable deep neural network (DNN) based nonlinear deformable simulation framework. Unlike other deep learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g. an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though DNN is known for its rich expressivity of nonlinear functions, directly using DNN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. DeepWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model -- the linear elasticity. In other words, DeepWarp yields an incremental displacement fix based on a simplified (therefore incorrect) simulation result other than returning the unknown displacement directly. We contrive a compact yet effective feature vector including geodesic, potential and digression to sort training pairs of per-node linear and nonlinear displacement. DeepWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one DNN training is able to handle a wide range of 3D models of various geometries including most examples shown in the paper. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, and DeepWarp is able to simulate large models in real time.

cs.GR↗
arXiv2018

ART-UP: A Novel Method for Generating Scanning-robust Aesthetic QR codes

QR codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this paper, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

cs.MM↗
arXiv2018

Efficiently Disassemble-and-Pack for Mechanism

In this paper, we present a disassemble-and-pack approach for a mechanism to seek a box which contains total mechanical parts with high space utilization. Its key feature is that mechanism contains not only geometric shapes but also internal motion structures which can be calculated to adjust geometric shapes of the mechanical parts. Our system consists of two steps: disassemble mechanical object into a group set and pack them within a box efficiently. The first step is to create a hierarchy of possible group set of parts which is generated by disconnecting the selected joints and adjust motion structures of parts in groups. The aim of this step is seeking total minimum volume of each group. The second step is to exploit the hierarchy based on breadth-first-search to obtain a group set. Every group in the set is inserted into specified box from maximum volume to minimum based on our packing strategy. Until an approximated result with satisfied efficiency is accepted, our approach finish exploiting the hierarchy.

cs.CG↗
arXiv2017

Quantum interference induced by multiple Landau-Zener transitions in a strongly driven rf-SQUID qubit

We irradiated an rf-SQUID qubit with large-amplitude and high frequency electromagnetic field. Population transitions between macroscopic distinctive quantum states due to Landau-Zener transitions at energy-level avoided crossings were observed. The qubit population on the excited states as a function of flux detuning and microwave power exhibits interference patterns. Some novel features are found in the interference and a model based on rate equations can well address the features.

quant-ph↗
arXiv2016

The clumpy torus around type II AGN as revealed by X-ray fluorescent lines

The reflection spectrum of the torus around AGN is characterized by X-ray fluorescent lines, which are most prominent for type II AGN. A clumpy torus allows photons reflected from the back-side of the torus to leak through the front free-of-obscuration regions. Therefore, the observed X-ray fluorescent lines are sensitive to the clumpiness of the torus. We analyse a sample of type II AGN observed with Chandra HETGS, and measure the fluxes for the Si Ka and Fe Ka lines. The measured Fe Ka/Si Ka ratios, spanning a range between $5-60$, are far smaller than the ratios predicted from simulations of smooth tori, indicating that the tori of the studied sources have clumpy distributions rather than smooth ones. Compared with simulation results of clumpy tori with a half-opening angle of 60$^{\circ}$, the Circinus galaxy has a Fe Ka/Si Ka ratio of $\sim60$, which is close to the simulation results for $N=5$, where $N$ is the average number of clumps along the line of sight. The Fe Ka/Si Ka ratios of the other sources are all below the simulation results for $N=2$. Overall, it shows that the non-Fe fluorescent lines in the soft X-ray band are a potentially powerful probe of the clumpiness of the torus around AGN.

astro-ph.HE↗
arXiv2015

The Spin of The Black Hole in the X-ray Binary Nova Muscae 1991

The bright soft X-ray transient Nova Muscae 1991 was intensively observed during its entire 8-month outburst using the Large Area Counter (LAC) onboard the Ginga satellite. Recently, we obtained accurate estimates of the mass of the black hole primary, the orbital inclination angle of the system, and the distance. Using these crucial input data and Ginga X-ray spectra, we have measured the spin of the black hole using the continuum-fitting method. For four X-ray spectra of extraordinary quality we have determined the dimensionless spin parameter of the black hole to be a/M = 0.63 (-0.19, +0.16) (1 sigma confidence level), a result that we confirm using eleven additional spectra of lower quality. Our spin estimate challenges two published results: It is somewhat higher than the value predicted by a proposed relationship between jet power and spin; and we find that the spin of the black hole is decidedly prograde, not retrograde as has been claimed.

astro-ph.HE↗
arXiv2015

X-ray fluorescent lines from the Compton-thick AGN in M51

The cold disk/torus gas surrounding active galactic nuclei (AGN) emits fluorescent lines when irradiated by hard X-ray photons. The fluorescent lines of elements other than Fe and Ni are rarely detected due to their relative faintness. We report the detection of K$α$ lines of neutral Si, S, Ar, Ca, Cr, and Mn, along with the prominent Fe K$α$, Fe K$β$, and Ni K$α$ lines, from the deep Chandra observation of the low-luminosity Compton-thick AGN in M51. The Si K$α$ line at 1.74 keV is detected at $\sim3σ$, the other fluorescent lines have a significance between 2 and 2.5 $σ$, while the Cr line has a significance of $\sim1.5σ$. These faint fluorescent lines are made observable due to the heavy obscuration of the intrinsic spectrum of M51, which is revealed by Nustar observation above 10 keV. The hard X-ray continuum of M51 from Chandra and Nustar can be fitted with a power-law spectrum with an index of 1.8, reprocessed by a torus with an equatorial column density of $N_{\rm H}\sim7\times10^{24}$ cm$^{-2}$ and an inclination angle of $74$ degrees. This confirms the Compton-thick nature of the nucleus of M51. The relative element abundances inferred from the fluxes of the fluorescent lines are similar to their solar values, except for Mn, which is about 10 times overabundant. It indicates that Mn is likely enhanced by the nuclear spallation of Fe.

astro-ph.HE↗
arXiv2015

Detection of small single-cycle signals by stochastic resonance using a bistable superconducting quantum interference device

We propose and experimentally demonstrate detecting small single-cycle and few-cycle signals by using the symmetric double-well potential of a radio frequency superconducting quantum interference device (rf-SQUID). We show that the response of this bistable system to single- and few-cycle signals has a non-monotonic dependence on the noise strength. The response, measured by the probability of transition from initial potential well to the opposite one, becomes maximum when the noise-induced transition rate between the two stable states of the rf-SQUID is comparable to the signal frequency. Comparison to numerical simulations shows that the phenomenon is a manifestation of stochastic resonance.

cond-mat.mes-hall↗