Source Video
Source video analysis encompasses a broad range of research aiming to extract meaningful information and perform various tasks directly from video data. Current efforts focus on developing robust and efficient methods for tasks such as 3D motion estimation, object detection and tracking, and multimodal analysis integrating audio and other sensor data, often employing deep learning architectures like transformers and diffusion models. These advancements have significant implications for diverse fields, including autonomous driving, medical diagnosis, and multimedia content creation, by enabling more sophisticated and automated processing of visual information.
Papers
REDUCIO! Generating 1024$\times$1024 Video within 16 Seconds using Extremely Compressed Motion Latents
Rui Tian, Qi Dai, Jianmin Bao, Kai Qiu, Yifan Yang, Chong Luo, Zuxuan Wu, Yu-Gang Jiang
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, Yaohui Wang, Xinyuan Chen, Ying-Cong Chen, Limin Wang, Dahua Lin, Yu Qiao, Ziwei Liu
Principles of Visual Tokens for Efficient Video Understanding
Xinyue Hao, Gen Li, Shreyank N Gowda, Robert B Fisher, Jonathan Huang, Anurag Arnab, Laura Sevilla-Lara
Machine vision-aware quality metrics for compressed image and video assessment
Mikhail Dremin (1), Konstantin Kozhemyakov (1), Ivan Molodetskikh (1), Malakhov Kirill (2), Artur Sagitov (2 and 3), Dmitriy Vatolin (1) ((1) Lomonosov Moscow State University, (2) Huawei Technologies Co., Ltd., (3) Independent Researcher Linjianping)
High-Frequency Enhanced Hybrid Neural Representation for Video Compression
Li Yu, Zhihui Li, Jimin Xiao, Moncef Gabbouj