Results 321 to 330 of about 10,228,794 (387)

VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

arXiv.org, 2023
Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source models available for researchers and engineers.
Haoxin Chen, Menghan Xia, Yin-Yin He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, Chao-Liang Weng, Ying Shan   +11 more
semanticscholar   +1 more source

I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

arXiv.org, 2023
Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity.
Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Z. Qin, Xiang Wang, Deli Zhao, Jingren Zhou   +8 more
semanticscholar   +1 more source

Perceptual Video Quality Metric for 3D video quality assessment

2010 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 2010
One method of evaluating the quality of stereoscopic video is the use of conventional two dimensional (2D) objective metrics. Metrics with good representation of the Human Visual System (HVS) will present more accurate evaluation. In this paper we propose a perceptual based objective metric for 2D videos for 3D video quality evaluation.
Joveluro P., Malekmohamadi H., Fernando W.A.C., Kondoz A.M.   +3 more
openaire   +1 more source

Completely Blind Video Quality Evaluator

IEEE Signal Processing Letters, 2022
Automatic video quality assessment of user-generated content (UGC) has gained increased interest recently, due to the ubiquity of shared video clips uploaded and circulated on social media platforms across the globe.
Qi Zheng, Zhengzhong Tu, Xiaoyang Zeng, A. Bovik, Yibo Fan   +4 more
semanticscholar   +1 more source

Long Short-term Convolutional Transformer for No-Reference Video Quality Assessment

ACM Multimedia, 2021
No-reference video quality assessment has not been widely benefited from deep learning, mainly due to the complexity, diversity and particularity of modelling spatial and temporal characteristics in quality assessment scenario.
Junyong You
semanticscholar   +1 more source

VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

Computer Vision and Pattern Recognition, 2023
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its
Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liangsheng Wang, Yujun Shen, Deli Zhao, Jinren Zhou, Tien-Ping Tan   +8 more
semanticscholar   +1 more source

LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models

International Journal of Computer Vision, 2023
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously (a) accomplish the synthesis of visually ...
Yaohui Wang, Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang, Yinan He, Jiashuo Yu, Pe-der Yang, Yuwei Guo, Tianxing Wu, Chenyang Si, Yuming Jiang, Cunjian Chen, Chen Change Loy, Bo Dai, Dahua Lin, Y. Qiao, Ziwei Liu   +19 more
semanticscholar   +1 more source

Quality-Aware Video

2007 IEEE International Conference on Image Processing, 2007
Development in network visual communications has emphasized on the need of objective, reliable and easy-to-use video quality assessment (VQA) systems. This paper introduces a novel idea of quality-aware video (QAV), in which extracted features about the original video sequence are invisibly embedded into the same video data. When such a QAV sequence is
Basavaraj Hiremath, Qiang Li, Zhou Wang
openaire   +1 more source

Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features

ACM Multimedia, 2020
Due to the wide range of different natural temporal and spatial distortions appearing in user generated video content, blind assessment of natural video quality is a challenging research problem.
J. Korhonen, Yicheng Su, Junyong You
semanticscholar   +1 more source

Q-Bench-Video: Benchmark the Video Quality Understanding of LMMs

Computer Vision and Pattern Recognition
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding.
Zicheng Zhang, Ziheng Jia, Haoning Wu, Chunyi Li, Zijian Chen, Yingjie Zhou, Wei Sun, Xiaohong Liu, Xiongkuo Min, Weisi Lin, Guangtao Zhai   +10 more
semanticscholar   +1 more source