DirecT2V.mp4
In the paradigm of AI-generated content (AIGC), there has been increasing attention in extending pre-trained text-to-image (T2I) models to text-to-video (T2V) generation. Despite their effectiveness, these frameworks face challenges in maintaining consistent narratives and handling rapid shifts in scene composition or object placement from a single user prompt. This paper introduces a new framework, dubbed DirecT2V, which leverages instruction-tuned large language models (LLMs) to generate frame-by-frame descriptions from a single abstract user prompt. DirecT2V utilizes LLM directors to divide user inputs into separate prompts for each frame, enabling the inclusion of time-varying content and facilitating consistent video generation. To maintain temporal consistency and prevent object collapse, we propose a novel value mapping method and dual-softmax filtering. Extensive experimental results validate the effectiveness of the DirecT2V framework in producing visually coherent and consistent videos from abstract user prompts, addressing the challenges of zero-shot video generation. The code and demo will be publicly availble.
Overall pipeline of DirecT2V. Our framework consists of two parts: directing an abstract user prompt with an LLM director (GPT-4) and video generation with a modified T2I diffusion (Stable Diffusion).
The running code can be found in run_direct2v.py. We used PyTorch 1.13.0 and Diffusers 1.19.3.
python run_direct2v.py
- Upload code
- Implement a demo using the ChatGPT API
- Improve efficiency
@article{hong2023large,
title={Large Language Models are Frame-level Directors for Zero-shot Text-to-Video Generation},
author={Hong, Susung and Seo, Junyoung and Hong, Sunghwan and Shin, Heeseong and Kim, Seungryong},
journal={arXiv preprint arXiv:2305.14330},
year={2023}
}