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cs.CV2025

ProGuard: Towards Proactive Multimodal Safeguard

Shaohan Yu, Lijun Li, Chenyang Si +2

The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challe…

cs.CV2025

LongVie 2: Multimodal Controllable Ultra-Long Video World Model

Jianxiong Gao, Zhaoxi Chen, Xian Liu +7

Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should p…

cs.CV2025

RealDPO: Real or Not Real, that is the Preference

Guo Cheng, Danni Yang, Ziqi Huang +3

Video generative models have recently achieved notable advancements in synthesis quality. However, generating complex motions remains a critical challenge, as existing models often…

cs.CV2025

Dual-Expert Consistency Model for Efficient and High-Quality Video Generation

Zhengyao Lv, Chenyang Si, Tianlin Pan +4

Diffusion Models have achieved remarkable results in video synthesis but require iterative denoising steps, leading to substantial computational overhead. Consistency Models have m…

cs.CV2025

LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation

Jianxiong Gao, Zhaoxi Chen, Xian Liu +5

Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such a…

cs.CV2025

Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers

Zhengyao Lv, Tianlin Pan, Chenyang Si +4

Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation. However, even state-of-the-art MM-DiT models like FLUX struggle with…