6 papers
PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts
Ruixiao Dong, Jianmin Bao, Bozhi Luan +6
Pixel-space diffusion models provide an appealing alternative to latent-space diffusion models by directly modeling raw images without relying on a learned tokenizer. However, scal…
ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression
Qin Yan, Ruixiao Dong, Yutao Xie +5
Progressive autoregressive image codecs provide an appealing paradigm for generative compression by quantizing continuous latents into discrete tokens, transmitting coarse-to-fine…
Dense-Prior-Guided Generative Video Compression
Ding Ding, Daowen Li, Ying Chen +4
Diffusion-based generative video compression offers a promising paradigm for low-bitrate reconstruction, but existing keyframe-based controllable approaches rely on sparse priors f…
ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling
Daowen Li, Ruixiao Dong, Kai Li +3
Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native suppor…
ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention
Keli Liu, Zhendong Wang, Wengang Zhou +3
Text-to-image generation with visual autoregressive~(VAR) models has recently achieved impressive advances in generation fidelity and inference efficiency. While control mechanisms…
EchoGen: Generating Visual Echoes in Any Scene via Feed-Forward Subject-Driven Auto-Regressive Model
Ruixiao Dong, Zhendong Wang, Keli Liu +5
Subject-driven generation is a critical task in creative AI; yet current state-of-the-art methods present a stark trade-off. They either rely on computationally expensive, per-subj…