9 papers
Building Pretraining Data for World Models: An Unreal Engine-Based Pipeline for Action-Conditioned Video Generation
Haoyu Wang, Songchun Zhang, Haoran Li +3
Action-conditioned video models require large-scale visual data paired with control signals that are temporally aligned with the resulting scene transitions. Such supervision is di…
Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds
Nan Duan, Haoyang Huang, Weiyang Jin +13
Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, follow user controls, and remain…
EchoWM: Open and Enterable Omnimodal World Models
Songchun Zhang, Yaowei Li, Junhao Zhuang +19
We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music an…
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
Haoyu Wang, Xingyu Yu, Haiyan Zhao +2
Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quantization (SQ), which enables e…
UniSVQ: 2-bit Unified Scalar-Vector Quantization
Haoyu Wang, Haiyan Zhao, Xingyu Yu +4
Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration for large language models (LLMs). Scalar quantization (SQ) and vector quantizat…
Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions
Luxury, Jie Huang, Zihao Fan +25
While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-tim…