3 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation
Wenxue Li, Peiyan Guan, Haoyang Jiang +15
Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, exis…
OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing
Chenxuan Miao, Yutong Feng, Yi Lu +6
Instruction-based video editing (IVE) is an emerging field with broad applications, yet evaluating editing models remains challenging. Existing benchmarks suffer from two major lim…
CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing
Fuchen Long, Cong Wang, Zitao Gao +8
The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supp…
GDRO: Group-level Reward Post-training Suitable for Diffusion Models
Yiyang Wang, Xi Chen, Xiaogang Xu +2
Recent advancements adopt online reinforcement learning (RL) from LLMs to text-to-image rectified flow diffusion models for reward alignment. The use of group-level rewards success…