Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smooth...
Zhuo-Ran Zhao, Sheng-Ju Qian, Tong-Tong Liang et al.· 0 citations
We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training across heterogeneous data sources and video backbones is challenging: datasets differ in temporal scale, camera geometry, visual quality, motion, and captioning styles...
Jun-Chao Huang, Gui-An Fang, Sheng-Ju Qian et al.· 1 citation
VideoRAE is introduced, a representation autoencoder that converts features from a frozen video foundation model into compact, reconstruction-capable latents for video generation, establishing frozen video foundation representations as compact, versatile, and generation-friendly video latents.
Zhihao Xie, Junfeng Wu, Xinting Hu et al.· arXiv.org· 0 citations
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