I4VGen: Image as Stepping Stone for Text-to-Video Generation

• URL: http://arxiv.org/abs/2406.02230v1
• Date: Tue, 4 Jun 2024 11:48:44 GMT
• Title: I4VGen: Image as Stepping Stone for Text-to-Video Generation
• Authors: Xiefan Guo, Jinlin Liu, Miaomiao Cui, Di Huang,
• Abstract summary: I4VGen is a training-free and plug-and-play video diffusion inference framework. It enhances text-to-video generation by leveraging robust image techniques. I4VGen produces videos with higher visual realism and textual fidelity.
• Score: 22.3850273729521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-video generation has lagged behind text-to-image synthesis in quality and diversity due to the complexity of spatio-temporal modeling and limited video-text datasets. This paper presents I4VGen, a training-free and plug-and-play video diffusion inference framework, which enhances text-to-video generation by leveraging robust image techniques. Specifically, following text-to-image-to-video, I4VGen decomposes the text-to-video generation into two stages: anchor image synthesis and anchor image-guided video synthesis. Correspondingly, a well-designed generation-selection pipeline is employed to achieve visually-realistic and semantically-faithful anchor image, and an innovative Noise-Invariant Video Score Distillation Sampling is incorporated to animate the image to a dynamic video, followed by a video regeneration process to refine the video. This inference strategy effectively mitigates the prevalent issue of non-zero terminal signal-to-noise ratio. Extensive evaluations show that I4VGen not only produces videos with higher visual realism and textual fidelity but also integrates seamlessly into existing image-to-video diffusion models, thereby improving overall video quality.

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