7 papers
MemLearner: Learning to Query Context memory for Video World Models
Jiwen Yu, Jianxiong Gao, Jianhong Bai +7
Video World Models are interactive video generation models that predict future world states based on user actions and history video frames. A critical challenge in video world mode…
CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation
Kaiyi Huang, Yukun Huang, Yu Li +8
Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical se…
FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
Kaiyi Huang, Yukun Huang, Xintao Wang +6
AI-driven content creation has shown potential in film production. However, existing film generation systems struggle to implement cinematic principles and thus fail to generate pr…
DreamCube: 3D Panorama Generation via Multi-plane Synchronization
Yukun Huang, Yanning Zhou, Jianan Wang +2
3D panorama synthesis is a promising yet challenging task that demands high-quality and diverse visual appearance and geometry of the generated omnidirectional content. Existing me…
T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation
Kaiyi Huang, Chengqi Duan, Kaiyue Sun +3
Despite the impressive advances in text-to-image models, they often struggle to effectively compose complex scenes with multiple objects, displaying various attributes and relation…
T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
Kaiyue Sun, Kaiyi Huang, Xian Liu +4
Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored.…