12 citations · 18 across the 11 of their papers we have counts for
14 papers
Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors
Yunuo Chen, Jingnan Gao, Bing He +6
Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are not designed to decode compre…
Compressing 3D Gaussian Splatting via Cross-Representation Priors
Yezheng Zhang, Huanxiong Liang, Chuqin Zhou +2
3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis but incurs high storage and transmission costs due to dense Gaussian primitives. Recent anchor-based compress…
Decoupled Self-Forcing Distillation for Streaming Talking Head Generation
Yanru An, Ruiyan Wang, Wenwu Wei +7
Streaming talking-head generation produces each frame as its driving audio arrives, yet fidelity and efficiency have so far pulled in opposite directions: end-to-end methods condit…
Benchmarking Neural Speech Compression from a Rate-Distortion Perspective
Jun Xu, Zhengxue Cheng, Fengxi Zhang +3
Learning-based speech compression has achieved promising low-bitrate performance, but many neural speech codecs still describe quantized latents with preset-rate discrete symbols o…
SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors
Bing He, Jingnan Gao, Yunuo Chen +5
Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches lev…
MultiEgo: A Multi-View Egocentric Video Dataset for 4D Scene Reconstruction
Bate Li, Houqiang Zhong, Zhengxue Cheng +4
Multi-view egocentric dynamic scene reconstruction holds significant research value for applications in holographic documentation of social interactions. However, existing reconstr…
Large Language Model for Lossless Image Compression with Visual Prompts
Junhao Du, Chuqin Zhou, Ning Cao +6
Recent advancements in deep learning have driven significant progress in lossless image compression. With the emergence of Large Language Models (LLMs), preliminary attempts have b…
AsymLLIC: Asymmetric Lightweight Learned Image Compression
Shen Wang, Zhengxue Cheng, Donghui Feng +3
Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymme…
Controllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression
Chuqin Zhou, Guo Lu, Jiangchuan Li +4
Neural image compression often faces a challenging trade-off among rate, distortion and perception. While most existing methods typically focus on either achieving high pixel-level…
Face De-identification: State-of-the-art Methods and Comparative Studies
Jingyi Cao, Xiangyi Chen, Bo Liu +5
The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the p…
H3D-DGS: Exploring Heterogeneous 3D Motion Representation for Deformable 3D Gaussian Splatting
Bing He, Yunuo Chen, Guo Lu +5
Dynamic scene reconstruction poses a persistent challenge in 3D vision. Deformable 3D Gaussian Splatting has emerged as an effective method for this task, offering real-time render…
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration
Yuhong Zhang, Hengsheng Zhang, Xinning Chai +4
Image restoration is a classic low-level problem aimed at recovering high-quality images from low-quality images with various degradations such as blur, noise, rain, haze, etc. How…
SSP-IR: Semantic and Structure Priors for Diffusion-based Realistic Image Restoration
Yuhong Zhang, Hengsheng Zhang, Zhengxue Cheng +3
Realistic image restoration is a crucial task in computer vision, and diffusion-based models for image restoration have garnered significant attention due to their ability to produ…
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation
Han Xue, Qianru Sun, Li Song +2
We propose In-Context Translation (ICT), a general learning framework to unify visual recognition (e.g., semantic segmentation), low-level image processing (e.g., denoising), and c…