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Found 9 papers, 3 papers with code
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
Generative Visual Compression: A Review
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs.
Generative Face Video Coding Techniques and Standardization Efforts: A Review
Generative Face Video Coding (GFVC) techniques can exploit the compact representation of facial priors and the strong inference capability of deep generative models, achieving high-quality face video communication in ultra-low bandwidth scenarios.
Semantic Face Compression for Metaverse: A Compact 3D Descriptor Based Approach
In this letter, we envision a new metaverse communication paradigm for virtual avatar faces, and develop the semantic face compression with compact 3D facial descriptors.
Interactive Face Video Coding: A Generative Compression Framework
In this paper, we propose a novel framework for Interactive Face Video Coding (IFVC), which allows humans to interact with the intrinsic visual representations instead of the signals.
MAU: A Motion-Aware Unit for Video Prediction and Beyond
The attention module aims to learn an attention map based on the correlations between the current spatial state and the historical spatial states.
Ranked #18 on
Video Prediction
on Moving MNIST
End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization
In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision.
Multi-Density Attention Network for Loop Filtering in Video Compression
In this paper, we propose a on-line scaling based multi-density attention network for loop filtering in video compression.
A Cross Channel Context Model for Latents in Deep Image Compression
The proposed cross channel context model is combined with the joint autoregressive and hierarchical prior entropy model.
