7 papers
OmniGen-AR: AutoRegressive Any-to-Image Generation
Junke Wang, Xun Wang, Qiushan Guo +4
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, ex…
MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation
Dewei Zhou, Xinyu Huang, Xun Wang +6
Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot direct…
Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation
Yabo Zhang, Kunchang Li, Dewei Zhou +2
While recent advancements in multimodal language models have enabled image generation from expressive multi-image instructions, existing methods struggle to maintain performance un…
Seedream 3.0 Technical Report
Yu Gao, Lixue Gong, Qiushan Guo +28
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in…
Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
Chao Liao, Liyang Liu, Xun Wang +7
Recent progress in unified models for image understanding and generation has been impressive, yet most approaches remain limited to single-modal generation conditioned on multiple…
SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL
Junke Wang, Zhi Tian, Xun Wang +4
This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference o…