197 research outputs found

    A petunia ethylene-responsive element binding factor, PhERF2, plays an important role in antiviral RNA silencing.

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    Virus-induced RNA silencing is involved in plant antiviral defense and requires key enzyme components, including RNA-dependent RNA polymerases (RDRs), Dicer-like RNase III enzymes (DCLs), and Argonaute proteins (AGOs). However, the transcriptional regulation of these critical components is largely unknown. In petunia (Petunia hybrida), an ethylene-responsive element binding factor, PhERF2, is induced by Tobacco rattle virus (TRV) infection. Inclusion of a PhERF2 fragment in a TRV silencing construct containing reporter fragments of phytoene desaturase (PDS) or chalcone synthase (CHS) substantially impaired silencing efficiency of both the PDS and CHS reporters. Silencing was also impaired in PhERF2- RNAi lines, where TRV-PhPDS infection did not show the expected silencing phenotype (photobleaching). In contrast, photobleaching in response to infiltration with the TRV-PhPDS construct was enhanced in plants overexpressing PhERF2 Transcript abundance of the RNA silencing-related genes RDR2, RDR6, DCL2, and AGO2 was lower in PhERF2-silenced plants but higher in PhERF2-overexpressing plants. Moreover, PhERF2-silenced lines showed higher susceptibility to Cucumber mosaic virus (CMV) than wild-type (WT) plants, while plants overexpressing PhERF2 exhibited increased resistance. Interestingly, growth and development of PhERF2-RNAi lines were substantially slower, whereas the overexpressing lines were more vigorous than the controls. Taken together, our results indicate that PhERF2 functions as a positive regulator in antiviral RNA silencing

    Color-NeuS: Reconstructing Neural Implicit Surfaces with Color

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    The reconstruction of object surfaces from multi-view images or monocular video is a fundamental issue in computer vision. However, much of the recent research concentrates on reconstructing geometry through implicit or explicit methods. In this paper, we shift our focus towards reconstructing mesh in conjunction with color. We remove the view-dependent color from neural volume rendering while retaining volume rendering performance through a relighting network. Mesh is extracted from the signed distance function (SDF) network for the surface, and color for each surface vertex is drawn from the global color network. To evaluate our approach, we conceived a in hand object scanning task featuring numerous occlusions and dramatic shifts in lighting conditions. We've gathered several videos for this task, and the results surpass those of any existing methods capable of reconstructing mesh alongside color. Additionally, our method's performance was assessed using public datasets, including DTU, BlendedMVS, and OmniObject3D. The results indicated that our method performs well across all these datasets. Project page: https://colmar-zlicheng.github.io/color_neus

    Time scales of epidemic spread and risk perception on adaptive networks

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    Incorporating dynamic contact networks and delayed awareness into a contagion model with memory, we study the spreading patterns of infectious diseases in connected populations. It is found that the spread of an infectious disease is not only related to the past exposures of an individual to the infected but also to the time scales of risk perception reflected in the social network adaptation. The epidemic threshold pcp_{c} is found to decrease with the rise of the time scale parameter s and the memory length T, they satisfy the equation pc=1T+ωTas(1−e−ωT2/as)p_{c} =\frac{1}{T}+ \frac{\omega T}{a^s(1-e^{-\omega T^2/a^s})}. Both the lifetime of the epidemic and the topological property of the evolved network are considered. The standard deviation σd\sigma_{d} of the degree distribution increases with the rise of the absorbing time tct_{c}, a power-law relation σd=mtcγ\sigma_{d}=mt_{c}^\gamma is found

    POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo

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    Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3D information of multi-view stereo into the 2D features. In contrast, we present a novel method, named POEM, that directly operates on the 3D POints Embedded in the Multi-view stereo for reconstructing hand mesh in it. Point is a natural form of 3D information and an ideal medium for fusing features across views, as it has different projections on different views. Our method is thus in light of a simple yet effective idea, that a complex 3D hand mesh can be represented by a set of 3D points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encircle the hand. To leverage the power of points, we design two operations: point-based feature fusion and cross-set point attention mechanism. Evaluation on three challenging multi-view datasets shows that POEM outperforms the state-of-the-art in hand mesh reconstruction. Code and models are available for research at https://github.com/lixiny/POEM.Comment: Accepted by CVPR 202

    CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

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    In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari
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