3,750 research outputs found
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
3D Morphable Model (3DMM) based methods have achieved great success in
recovering 3D face shapes from single-view images. However, the facial textures
recovered by such methods lack the fidelity as exhibited in the input images.
Recent work demonstrates high-quality facial texture recovering with generative
networks trained from a large-scale database of high-resolution UV maps of face
textures, which is hard to prepare and not publicly available. In this paper,
we introduce a method to reconstruct 3D facial shapes with high-fidelity
textures from single-view images in-the-wild, without the need to capture a
large-scale face texture database. The main idea is to refine the initial
texture generated by a 3DMM based method with facial details from the input
image. To this end, we propose to use graph convolutional networks to
reconstruct the detailed colors for the mesh vertices instead of reconstructing
the UV map. Experiments show that our method can generate high-quality results
and outperforms state-of-the-art methods in both qualitative and quantitative
comparisons.Comment: Accepted to CVPR 2020. The source code is available at
https://github.com/FuxiCV/3D-Face-GCN
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on
Circuits and Systems (ISCAS
Isolation of Thylakoid Membrane Complexes from Rice by a New Double-Strips BN/SDS-PAGE and Bioinformatics Prediction of Stromal Ridge Subunits Interaction
Thylakoid membrane complexes of rice (Oryza sativa L.) play crucial roles in growth and crop production. Understanding of protein interactions within the complex would provide new insights into photosynthesis. Here, a new “Double-Strips BN/SDS-PAGE” method was employed to separate thylakoid membrane complexes in order to increase the protein abundance on 2D-gels and to facilitate the identification of hydrophobic transmembrane proteins. A total of 58 protein spots could be observed and subunit constitution of these complexes exhibited on 2D-gels. The generality of this new approach was confirmed using thylakoid membrane from spinach (Spinacia oleracea) and pumpkin (Cucurita spp). Furthermore, the proteins separated from rice thylakoid membrane were identified by the mass spectrometry (MS). The stromal ridge proteins PsaD and PsaE were identified both in the holo- and core- PSI complexes of rice. Using molecular dynamics simulation to explore the recognition mechanism of these subunits, we showed that salt bridge interactions between residues R19 of PsaC and E168 of PasD as well as R75 of PsaC and E91 of PsaD played important roles in the stability of the complex. This stromal ridge subunits interaction was also supported by the subsequent analysis of the binding free energy, the intramolecular distances and the intramolecular energy
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