6 papers · 1 filter
MiMaAlign: Hierarchical Micro-Macro Alignment for Physically Plausible Video Generation
Yansong Liu, Litong Gong, Weijie Li +2
Text-to-video (T2V) diffusion models have made remarkable progress in generating high-fidelity visuals, yet their outputs still often exhibit physically implausible phenomena, incl…
DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models
Huanran Hu, Zihui Ren, Dingyi Yang +4
Despite remarkable progress, video generation models often produce highly similar outputs when repeatedly sampled from the same prompt, limiting their usefulness for creative explo…
MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation
Yang Chen, Xiaowei Xu, Shuai Wang +4
Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often forces the model to exhaust its c…
Rethinking Scribble-Guided Image Editing: Generalization, Instruction Adherence, and Multi-Tasking
Mingyi Xu, Jinpeng Lin, Min Zhou +2
Scribble-guided image editing allows users to combine simple scribble annotations with text prompts to specify both where and how an image should be edited, enabling flexible inter…
RISE-T2V: Rephrasing and Injecting Semantics with LLM for Expansive Text-to-Video Generation
Xiangjun Zhang, Litong Gong, Yinglin Zheng +6
Most text-to-video(T2V) diffusion models depend on pre-trained text encoders for semantic alignment, yet they often fail to maintain video quality when provided with concise prompt…
TBStar-Edit: From Image Editing Pattern Shifting to Consistency Enhancement
Hao Fang, Zechao Zhan, Weixin Feng +3
Recent advances in image generation and editing technologies have enabled state-of-the-art models to achieve impressive results in general domains. However, when applied to e-comme…