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cs.CV2026

Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution

Tianshuo Xu, Yichen Xie, Depu Meng +5

Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption. This is not simply a capacity p…

cs.CV2026

LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation

Bo Jiang, Depu Meng, Yihan Hu +3

Modern video generators produce visually compelling clips but still struggle with physical and motion consistency, limiting their use as reliable world simulators. Existing remedie…

cs.CV2025

KlingAvatar 2.0 Technical Report

Kling Team, Jialu Chen, Yikang Ding +25

Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos…

cs.CV2025

Decoupling Complexity from Scale in Latent Diffusion Model

Tianxiong Zhong, Xingye Tian, Xuebo Wang +3

Existing latent diffusion models typically couple scale with content complexity, using more latent tokens to represent higher-resolution images or higher-frame rate videos. However…

cs.CV2025

VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption

Tianxiong Zhong, Xingye Tian, Boyuan Jiang +4

Modern video generation frameworks based on Latent Diffusion Models suffer from inefficiencies in tokenization due to the Frame-Proportional Information Assumption. Existing tokeni…

cs.CV2025

VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing

Jiahao Hu, Tianxiong Zhong, Xuebo Wang +5

Diffusion-based image editing models have made remarkable progress in recent years. However, achieving high-quality video editing remains a significant challenge. One major hurdle…