5 papers
Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation
Ao Ma, Jiasong Feng, Ke Cao +4
Storytelling tasks involving generating consistent subjects have gained significant attention recently. However, existing methods, whether training-free or training-based, continue…
U-StyDiT: Ultra-high Quality Artistic Style Transfer Using Diffusion Transformers
Zhanjie Zhang, Ao Ma, Ke Cao +6
Ultra-high quality artistic style transfer refers to repainting an ultra-high quality content image using the style information learned from the style image. Existing artistic styl…
WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation
Jing Wang, Ao Ma, Ke Cao +9
Recent rapid advancements in text-to-video (T2V) generation, such as SoRA and Kling, have shown great potential for building world simulators. However, current T2V models struggle…
Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism
Jun Zheng, Jing Wang, Fuwei Zhao +2
Video try-on stands as a promising area for its tremendous real-world potential. Previous research on video try-on has primarily focused on transferring product clothing images to…
Qihoo-T2X: An Efficient Proxy-Tokenized Diffusion Transformer for Text-to-Any-Task
Jing Wang, Ao Ma, Jiasong Feng +3
The global self-attention mechanism in diffusion transformers involves redundant computation due to the sparse and redundant nature of visual information, and the attention map of…