7 papers
In-context Region-based Drag: Drag Any Region to Any Shape
Jiacheng Sui, Tianyu Hao, Bingjie Gao +2
Diffusion models have shown promise in drag-style editing. Previous works mainly focus on point-based drag, which is inherently ambiguous. This paper focuses on region-based drag a…
Large-Scale Universal Defect Generation: Foundation Models and Datasets
Yuanting Fan, Jun Liu, Bin-Bin Gao +5
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data.…
Uni-Classifier: Leveraging Video Diffusion Priors for Universal Guidance Classifier
Yujie Zhou, Pengyang Ling, Jiazi Bu +2
In practical AI workflows, complex tasks often involve chaining multiple generative models, such as using a video or 3D generation model after a 2D image generator. However, distri…
CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Xiaoxue Wu, Bingjie Gao, Yu Qiao +2
Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transit…
AnimateScene: Camera-controllable Animation in Any Scene
Qingyang Liu, Bingjie Gao, Weiheng Huang +10
Recent advances in 3D scene reconstruction and 4D human animation have broadened adoption, but integrating the two remains difficult. Key challenges include placing humans at plaus…
The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation
Bingjie Gao, Xinyu Gao, Xiaoxue Wu +5
The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models…