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cs.CV2025
A Reason-then-Describe Instruction Interpreter for Controllable Video Generation
Shengqiong Wu, Weicai Ye, Yuanxing Zhang +7
Diffusion Transformers have significantly improved video fidelity and temporal coherence, however, practical controllability remains limited. Concise, ambiguous, and compositionall…
cs.CV2025
Imbalance in Balance: Online Concept Balancing in Generation Models
Yukai Shi, Jiarong Ou, Rui Chen +6
In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we att…
cs.CV2025
Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation
Shengqiong Wu, Weicai Ye, Jiahao Wang +8
To address the bottleneck of accurate user intent interpretation within the current video generation community, we present Any2Caption, a novel framework for controllable video gen…