17 papers
Large Language Models are Universal Reasoners for Visual Generation
Sucheng Ren, Chen Chen, Zhenbang Wang +5
Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM backbone handles both visual un…
Frequency-Aware Flow Matching for High-Quality Image Generation
Sucheng Ren, Qihang Yu, Ju He +3
Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively adds Gaussian noise. However…
A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens
Tommie Kerssies, Gabriele Berton, Ju He +5
Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce a deterministic prediction that implicitly averages over poss…
Autoregressive Image Generation with Masked Bit Modeling
Qihang Yu, Qihao Liu, Ju He +4
This paper challenges the dominance of continuous pipelines in visual generation. We systematically investigate the performance gap between discrete and continuous methods. Contrar…
ReVision: Refining Video Diffusion with Explicit 3D Motion Modeling
Qihao Liu, Ju He, Qihang Yu +2
In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges,…
FlowTok: Flowing Seamlessly Across Text and Image Tokens
Ju He, Qihang Yu, Qihao Liu +1
Bridging different modalities lies at the heart of cross-modality generation. While conventional approaches treat the text modality as a conditioning signal that gradually guides t…