2 citations · 2 across the 3 of their papers we have counts for
5 papers
ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment
Yiyang Chen, Xuanhua He, Xiujun Ma +1
Training-free video object editing aims to achieve precise object-level manipulation, including object insertion, swapping, and deletion. However, it faces significant challenges i…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…
FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers
Xuanhua He, Quande Liu, Zixuan Ye +7
Fine-grained and efficient controllability on video diffusion transformers has raised increasing desires for the applicability. Recently, In-context Conditioning emerged as a power…
UNIC: Unified In-Context Video Editing
Zixuan Ye, Xuanhua He, Quande Liu +7
Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional ad…
GameGen-X: Interactive Open-world Game Video Generation
Haoxuan Che, Xuanhua He, Quande Liu +2
We introduce GameGen-X, the first diffusion transformer model specifically designed for both generating and interactively controlling open-world game videos. This model facilitates…