Meta Inverse Prompting for Video Generative Models

Key points

• Recent advances in video generative models have enabled flexible conditional generation and editing pipelines, such as text-to-video, image-to-video, and reference-guided synthesis.1
• While inverse prompting has been studied for image generation, directly extending these approaches to video is challenging due to the need to capture temporal dynamics beyond static appearance, as well as the substantial computational cost of per-video optimization.1
• In this work, we propose the first practical framework for efficient video inverse prompting.1
• Experimental results demonstrate that MVIP recovers higher-quality inverse prompts with significantly improved reconstruction fidelity compared to image-based inversion and iterative captioning baselines, while achieving substantial gains in efficiency.1

Sources (1)

• [1]Meta Inverse Prompting for Video Generative Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 02:35 AM

  Specifically, we formulate inverse prompting as a meta-learning problem and introduce Meta Video Inverse Prompting (MVIP), which learns a meta-prompt optimized over temporal dimensions to enable fast and effective prompt inference.

  Recent advances in video generative models have enabled flexible conditional generation and editing pipelines, such as text-to-video, image-to-video, and reference-guided synthesis.

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