1 citations · 2 across the 6 of their papers we have counts for
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Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
Kaiwen Zheng, Guande He, Min Zhao +7
Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-conditioned interactive world…
UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers
Min Zhao, Bokai Yan, Xue Yang +5
Recent image diffusion transformers achieve high-fidelity generation, but struggle to generate images beyond these scales, suffering from content repetition and quality degradation…
UltraViCo: Breaking Extrapolation Limits in Video Diffusion Transformers
Min Zhao, Hongzhou Zhu, Yingze Wang +6
Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure mo…
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Min Zhao, Guande He, Yixiao Chen +3
Recent advancements in video generation have enabled models to synthesize high-quality, minute-long videos. However, generating even longer videos with temporal coherence remains a…
Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models
Fan Bao, Chendong Xiang, Gang Yue +7
We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-Vi…