Large text-to-video models trained on Internet-scale data have demonstrated extraordinary abilities to generate high-fidelity movies from arbitrarily written descriptions. However, fitting a huge pretrained model can be prohibitively expensive, making it difficult to fit these models to applications with limited domain-specific data, such as robotics or animation video. Researchers from Google DeepMind, UC Berkeley, MIT, and the University of Alberta discuss how a large pretrained text-to-video model can be customized for a variety of domains and downstream tasks without tweaking, inspired by how a small tweakable component (such as prompts, tuning of prefixes) can allow a large language model to perform new tasks without needing to access model weights. To address this, they present the Video Adapter, a method for generating task-specific small video models by using the scoring function of a large, pre-trained video broadcast model as a prior probability. Experiments show that video adapters can use as little as 1.25 percent of the pretrained model parameters to include the extensive knowledge and maintain the high fidelity of a large pretrained video model in a small, specific video model. task. High-quality movies for specific tasks can be generated using video adapters for various uses, including but not limited to animation, egocentric modeling, and real-world and simulated robotic data modeling.
Researchers test the video adapter in various video creation jobs. On hard data from Ego4D and robotic data from Bridge, the Video Adapter generates videos with better FVDs and starting scores than a high-quality pretrained large video model while using up to 80 times fewer parameters. The researchers qualitatively demonstrate that the Video Adapter enables the production of genre-specific videos such as those found in science fiction and animation. In addition, the study authors show how the Video Adapter can pave the way to bridge the infamous gap between robotics simulation and reality by modeling both real and simulated robotic movies and enabling data augmentation into real robotic videos through the individualized styling.
key features
- To achieve versatile, high-quality video synthesis without the need for gradient updates to the pretrained model, the video adapter combines the scores of a pretrained text-to-video model with the scores of a domain-specific tiny model (with 1 % of parameters) in the sampling. time.
- Pretrained video models can be easily adapted using the Video Adapter to movies of humans and robotic data.
- With the same number of TPU hours, the video adapter achieves higher startup, FVD, and FVD scores than pretrained and task-specific models.
- Potential uses for video adapters range from use in anime production to domain randomization to bridging the gap between simulation and reality in robotics.
- Unlike a huge video model pre-trained from internet data, the Video Adapter requires training a small, domain-specific text-to-video model with far fewer parameters. Video Adapter achieves high-quality, adaptive video synthesis by composing pre-trained, domain-specific video model scores during sampling.
- With the Video Adapter, you can give a video a unique look by using a model exposed to only one type of animation.
- With a video adapter, a large pretrained model can take on the visual characteristics of a much smaller animated model.
- With the help of a video adapter, a massive pretrained model can take on the visual aesthetics of a tiny sci-fi animated model.
- Video adapters can generate various movies in various genres and styles, including videos with egocentric movements based on manipulation and navigation, videos with individualized genres such as animation and science fiction, and videos with simulated and genuine robotic movements.
limitations
A small video model still needs to be trained on domain-specific data; therefore, while the Video Adapter can effectively adapt large text models to pre-trained video, it is not free of training. Another difference between the video adapter and other text-to-image and text-to-video APIs is that it requires the score to be output along with the resulting video. The video adapter makes text-to-video research more accessible to small industrial and academic institutions by addressing the lack of free access to model weights and computing efficiency.
In summary
It is obvious that as basic text-to-video models grow in size, they will need to be effectively adapted to the specific use of the task. Researchers have developed the Video Adapter, a powerful method for generating task- and domain-specific movies by using huge pre-trained text-to-video models as a probabilistic preview. Video adapters can synthesize high-quality video in specialized disciplines or desired aesthetics without requiring further tuning of the massive pretrained model.
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Dhanshree Shenwai is a Computer Engineer and has good experience in FinTech companies covering Finance, Cards & Payments and Banking domain with strong interest in AI applications. She is enthusiastic about exploring new technologies and advancements in today’s changing world, making everyone’s life easier.
