Summary of Vimi: Grounding Video Generation Through Multi-modal Instruction, by Yuwei Fang et al.
VIMI: Grounding Video Generation through Multi-modal Instruction
by Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chien Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed paper constructs a large-scale multimodal prompt dataset to enable diverse video generation tasks within a single model. The authors employ retrieval methods to pair in-context examples with given text prompts and utilize a two-stage training strategy for pretraining. A multimodal conditional video generation framework is proposed for pretraining on these augmented datasets, establishing a foundational model for grounded video generation. The model is then finetuned on three video generation tasks, incorporating multi-modal instructions. This process refines the model’s ability to handle diverse inputs and tasks, ensuring seamless integration of multi-modal information. VIMI demonstrates multimodal understanding capabilities, producing contextually rich and personalized videos grounded in provided inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine that can turn written text into moving video. That’s what this paper is about! The researchers created a big database of pairs between text prompts and short video clips to help train their model. They used two steps: first, they made the model learn from these pairs to generate videos based on text prompts. Then, they fine-tuned the model by having it watch many examples of different types of videos with corresponding text descriptions. The result is a super smart model that can create personalized and contextually rich videos based on text inputs. |
Keywords
» Artificial intelligence » Multi modal » Pretraining » Prompt
