Summary of Optical-flow Guided Prompt Optimization For Coherent Video Generation, by Hyelin Nam et al.
Optical-Flow Guided Prompt Optimization for Coherent Video Generation
by Hyelin Nam, Jaemin Kim, Dohun Lee, Jong Chul Ye
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called MotionPrompt for generating temporally consistent videos using text-to-video diffusion models. The framework guides the generation process via optical flow, training a discriminator to distinguish real and generated video frames. This allows the model to generate visually coherent video sequences that reflect natural motion dynamics without compromising content fidelity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method called MotionPrompt that helps text-to-video models create videos with consistent motion. It uses something called “optical flow” to guide the generation process, which makes the resulting videos look more realistic and natural. The approach is tested across different models and shows promising results. |
Keywords
» Artificial intelligence » Diffusion » Optical flow
