Enhancing Task-Specific Adaptation of Video-Based Models: Introducing Video Adapters as a Probabilistic Framework for Adapting Models from Text to Video

A large-scale text-to-video model trained on internet-scale data has shown an extraordinary ability to generate high-fidelity movies from arbitrarily written descriptions. However, fine-tuning huge pre-trained models can be prohibitively expensive, making it difficult to adapt these models to applications with limited domain-specific data, such as animation and robot videos. It becomes difficult. Researchers at Google DeepMind, University of California, Berkeley, Massachusetts Institute of Technology, and the University of Alberta found that using small modifiable components (prompts, prefix tuning, etc.) allowed large language models to scale without access to model weights. You will be able to run new tasks. To address this, they presented Video Adapter, a method to generate small task-specific video models using the score function of a large pre-trained video diffusion model as prior probabilities. Experiments demonstrate that the video adapter can use only 1.25 percent of the pre-trained model’s parameters to incorporate broad knowledge and retain the high fidelity of a large pre-trained video model in a small task-specific video model. increase. Video adapters enable the generation of high-quality, task-specific movies for a variety of uses, including animation, egocentric modeling, and modeling of simulated real-world robot data.

Researchers are testing video adapters in a variety of video creation jobs. Video adapters produce videos with better FVD and inception scores than high-quality pre-trained large video models while using up to 80x fewer parameters for challenging Ego4D and robotic bridge data to generate Researchers have qualitatively demonstrated that video adapters can be used to create genre-specific videos such as those found in science fiction and animation. Furthermore, the authors of this study demonstrated that the video adapter models both real and simulated robotic films, enabling data augmentation of real robotic videos via separate stylization, It shows how we can pave the way for bridging the gap between robotics’ infamous simulation and reality.

Main features

• To achieve high-quality yet versatile video synthesis without the need for gradient updates of pre-trained models, the video adapter uses pre-trained text-to-video model scores and Combine the scores of domain-specific small models (with a parameter of 1%). time.
• Pre-trained video models can be easily adapted to movies of human or robot data using video adapters.
• Under the same number of TPU hours, the video adapter gains higher FVD, FID, and inception scores than pre-trained task-specific models.
• Potential uses for video adapters range from use in animation production to domain randomization to bridge the gap between simulation and reality in robotics.
• In contrast to giant video models pretrained from internet data, video adapters require orders of magnitude fewer parameters to train small domain-specific text-to-video models. The video adapter achieves high-quality and adaptive video synthesis by composing pre-trained domain-specific video model scores during sampling.
• Video adapters allow you to give your videos a unique look with models exposed to only one type of animation.
• A video adapter allows a pre-trained model of considerable size to inherit the visual characteristics of an animated model of much smaller size.
• With the help of video adapters, large pre-trained models can take on the visual beauty of small sci-fi animated models.
• Video adapters can be used to display videos of various genres and styles, including videos containing egocentric motion based manipulation and navigation, videos containing separate genres such as animation and sci-fi, and videos containing simulated real robot motions. You can generate various movies.

Limitations

A small video model should be trained on domain-specific data. Therefore, the video adapter can effectively adapt large pre-trained text-to-video models, but training is not required. Another difference between the Video Adapter and other text-to-image and text-to-video APIs is that the score must be output along with the resulting video. By addressing the lack of free access to model weights and computational efficiency, Video Adapter makes text-to-video research effectively accessible to small industries and academic institutions.

In summary

As the text-to-video underlying model grows in size, it is clear that it must be effectively adapted to task-specific usage. Researchers have developed a video adapter, a powerful method for generating domain- and task-specific films, by using huge pre-trained text-to-video models as stochastic priors. The Video Adapter can synthesize high-quality videos with the beauty of your domain or purpose without the need for further fine-tuning of large pre-trained models.

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