3 papers
cs.CV2025
EditAR: Unified Conditional Generation with Autoregressive Models
Jiteng Mu, Nuno Vasconcelos, Xiaolong Wang
Recent progress in controllable image generation and editing is largely driven by diffusion-based methods. Although diffusion models perform exceptionally well in specific tasks wi…
cs.CV2024
Image Neural Field Diffusion Models
Yinbo Chen, Oliver Wang, Richard Zhang +3
Diffusion models have shown an impressive ability to model complex data distributions, with several key advantages over GANs, such as stable training, better coverage of the traini…
cs.CV2024
Editable Image Elements for Controllable Synthesis
Jiteng Mu, Michaël Gharbi, Richard Zhang +4
Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space…