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arXiv:2405.04233cs.CV

Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Fan Bao, Chendong Xiang, Gang Yue, Guande He, Hongzhou Zhu, Kaiwen Zheng, Min Zhao, Shilong Liu, Yaole Wang, Jun Zhu

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Abstract

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-ViT as its backbone, which unlocks the scalability and the capability for handling long videos. Vidu exhibits strong coherence and dynamism, and is capable of generating both realistic and imaginative videos, as well as understanding some professional photography techniques, on par with Sora -- the most powerful reported text-to-video generator. Finally, we perform initial experiments on other controllable video generation, including canny-to-video generation, video prediction and subject-driven generation, which demonstrate promising results.

arXiv comments
Project page at https://www.shengshu-ai.com/vidu
Journal reference
DOI
Subjects
cs.CVComputer Vision and Pattern Recognition cs.LGMachine Learning

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  • Show HN: Vidu – The New text/image-to-video MultiModal LLM

    Hi HN, Vidu team here. We're excited to introduce our latest project, presented by Tsinghua University and Shengshu Technology - Vidu, a text and image-to-video MultiModal LLM. Professor Jun Zhu and his colleagues published a research paper on Vidu and its capabilities during Vidu's early stages: https://arxiv.org/abs/2405.04233 To set Vidu apart from the mass generative video AI that has already been released, our team chose to focus on augmenting specific properties during the early-stages of…

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