11 papers
LensErase: A Foundation Model for Erase Anything
Yunuo Chen, Xiuyu Wu, Qinhong Yang +11
We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse ob…
Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth
Shengshi Yao, Jincheng Dai, Sixian Wang +5
In extreme-low bandwidth network scenarios, generative semantic codecs have emerged as promising solutions to reduce bandwidth cost for visual communications. However, these learne…
GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates
Tianjian Dang, Sixian Wang, Lei Luo +2
Recent generative video codecs (GVCs) have achieved impressive reconstruction fidelity at ultra-low bitrates (< 0.02 bits per pixel) by compressing the tokens from generative token…
Media Meets Communication in 6G: Fundamentals, Key Technologies, and Applications
Bingyan Xie, Longyu Zhou, Zihan Chen +9
The rapid advancement of sixth-generation (6G) networks is accelerating the convergence of media intelligence and communication intelligence, driving media communication beyond con…
Generative Communications: Overview, Technologies, and Trends
Wenjun Zhang, Zhiyong Chen, Tong Wu +4
The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication par…
Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures
Chuqin Zhou, Xiaoyue Ling, Yunuo Chen +3
While recent neural codecs achieve strong performance at low bitrates when optimized for perceptual quality, their effectiveness deteriorates significantly under ultra-low bitrate…
Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation
Tong Wu, Zhiyong Chen, Guo Lu +4
Conventional communication systems, including both separation-based coding and AI-driven joint source-channel coding (JSCC), are largely guided by Shannon's rate-distortion theory.…
DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations
Kailin Tan, Jincheng Dai, Sixian Wang +5
Generative joint source-channel coding (GJSCC) has emerged as a new Deep JSCC paradigm for achieving high-fidelity and robust image transmission under extreme wireless channel cond…
Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression
Huanxiong Liang, Yunuo Chen, Yicheng Pan +4
Recent advances in 2D Gaussian Splatting (2DGS) have demonstrated its potential as a compact image representation with millisecond-level decoding. However, existing 2DGS-based pipe…
Neural Hamiltonian Deformation Fields for Dynamic Scene Rendering
Hai-Long Qin, Sixian Wang, Guo Lu +1
Representing and rendering dynamic scenes with complex motions remains challenging in computer vision and graphics. Recent dynamic view synthesis methods achieve high-quality rende…
Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications
Hai-Long Qin, Jincheng Dai, Guo Lu +6
Semantic communications mark a paradigm shift from bit-accurate transmission toward meaning-centric communication, essential as wireless systems approach theoretical capacity limit…