591 research outputs found
TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation
Video frame interpolation (VFI) aims to synthesize an intermediate frame
between two consecutive frames. State-of-the-art approaches usually adopt a
two-step solution, which includes 1) generating locally-warped pixels by
flow-based motion estimations, 2) blending the warped pixels to form a full
frame through deep neural synthesis networks. However, due to the inconsistent
warping from the two consecutive frames, the warped features for new frames are
usually not aligned, which leads to distorted and blurred frames, especially
when large and complex motions occur. To solve this issue, in this paper we
propose a novel Trajectory-aware Transformer for Video Frame Interpolation
(TTVFI). In particular, we formulate the warped features with inconsistent
motions as query tokens, and formulate relevant regions in a motion trajectory
from two original consecutive frames into keys and values. Self-attention is
learned on relevant tokens along the trajectory to blend the pristine features
into intermediate frames through end-to-end training. Experimental results
demonstrate that our method outperforms other state-of-the-art methods in four
widely-used VFI benchmarks. Both code and pre-trained models will be released
soon
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
Existing real-world video super-resolution (VSR) methods focus on designing a
general degradation pipeline for open-domain videos while ignoring data
intrinsic characteristics which strongly limit their performance when applying
to some specific domains (eg., animation videos). In this paper, we thoroughly
explore the characteristics of animation videos and leverage the rich priors in
real-world animation data for a more practical animation VSR model. In
particular, we propose a multi-scale Vector-Quantized Degradation model for
animation video Super-Resolution (VQD-SR) to decompose the local details from
global structures and transfer the degradation priors in real-world animation
videos to a learned vector-quantized codebook for degradation modeling. A
rich-content Real Animation Low-quality (RAL) video dataset is collected for
extracting the priors. We further propose a data enhancement strategy for
high-resolution (HR) training videos based on our observation that existing HR
videos are mostly collected from the Web which contains conspicuous compression
artifacts. The proposed strategy is valid to lift the upper bound of animation
VSR performance, regardless of the specific VSR model. Experimental results
demonstrate the superiority of the proposed VQD-SR over state-of-the-art
methods, through extensive quantitative and qualitative evaluations of the
latest animation video super-resolution benchmark. The code and pre-trained
models can be downloaded at https://github.com/researchmm/VQD-SR
Beam energy distribution influences on density modulation efficiency in seeded free-electron lasers
The beam energy spread at the entrance of undulator system is of paramount
importance for efficient density modulation in high-gain seeded free-electron
lasers (FELs). In this paper, the dependences of high harmonic micro-bunching
in the high-gain harmonic generation (HGHG), echo-enabled harmonic generation
(EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the
electron energy spread distribution are studied. Theoretical investigations and
multi-dimensional numerical simulations are applied to the cases of uniform and
saddle beam energy distributions and compared to a traditional Gaussian
distribution. It shows that the uniform and saddle electron energy
distributions significantly enhance the performance of HGHG-FELs, while they
almost have no influence on EEHG and PEHG schemes. A numerical example
demonstrates that, with about 84keV RMS uniform and/or saddle slice energy
spread, the 30th harmonic radiation can be directly generated by a single-stage
seeding scheme for a soft x-ray FEL facility
Amplification and adaptation of centromeric repeats in polyploid switchgrass species.
Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187Â bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat
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