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20232026
most citedStep-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

1 citations · 1 across the 16 of their papers we have counts for

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cs.CV2025

Frame-Level Captions for Long Video Generation with Complex Multi Scenes

Guangcong Zheng, Jianlong Yuan, Bo Wang +3

Generating long videos that can show complex stories, like movie scenes from scripts, has great promise and offers much more than short clips. However, current methods that use aut…

cs.CV2025

STORYANCHORS: Generating Consistent Multi-Scene Story Frames for Long-Form Narratives

Bo Wang, Haoyang Huang, Zhiying Lu +6

This paper introduces StoryAnchors, a unified framework for generating high-quality, multi-scene story frames with strong temporal consistency. The framework employs a bidirectiona…

cs.CV2025

Generative Pre-trained Autoregressive Diffusion Transformer

Yuan Zhang, Jiacheng Jiang, Guoqing Ma +5

In this work, we present GPDiT, a Generative Pre-trained Autoregressive Diffusion Transformer that unifies the strengths of diffusion and autoregressive modeling for long-range vid…

cs.CV2025

Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model

Haoyang Huang, Guoqing Ma, Nan Duan +51

We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and…

cs.CV2025★ 1 cited

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

Guoqing Ma, Haoyang Huang, Kun Yan +112

We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression…