3 citations · 5 across the 6 of their papers we have counts for
6 papers
MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation
Haojie Zhang, Di Wu, Bingyan Liu +5
While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Further progress is constrained…
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis
Tongtong Su, Chengyu Wang, Bingyan Liu +2
In recent years, large text-to-video (T2V) synthesis models have garnered considerable attention for their abilities to generate videos from textual descriptions. However, achievin…
Understanding Attention Mechanism in Video Diffusion Models
Bingyan Liu, Chengyu Wang, Tongtong Su +4
Text-to-video (T2V) synthesis models, such as OpenAI's Sora, have garnered significant attention due to their ability to generate high-quality videos from a text prompt. In diffusi…
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing
Bingyan Liu, Chengyu Wang, Tingfeng Cao +2
Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative Text-to-image generation. Yet, for domain-specific scena…
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Tingfeng Cao, Chengyu Wang, Bingyan Liu +3
Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often requ…
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud
Chengyu Wang, Zhongjie Duan, Bingyan Liu +4
Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships. While existing diffusion models ha…