Open AI has taken one more step to make AI more appealing by solving real world problems with motion one-minute videos from text prompt. Sora is a text-to-video model which can generate up to one-minute videos with good visual quality and following prompt instructions.
OpenAI introduced Sora with sample video which company claims were generated using Sora without any modifications with prompts like “Drone view of waves crashing against the rugged cliffs along Big Sur’s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff’s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff’s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway.”
With high quality motion, photo realistic images, stability and almost perfect video angles, it is hard to distinguish between Sora’s AI generated video and high-resolution drone/ camera captured videos.
What is Sora?
Sora is based on the same transformer architecture that powers OpenAI’s ChatGPT as mentioned in our previous blog. It’s a diffusion model, which generates a video by starting off with one that looks like static noise. In order to be relevant by not diverting from the original topic, Open AI gave it foresight of many frames at a time. Akin to ChatGPT model with tokens, Sora is made of patches of videos and images as collections of smaller units of data.
Introducing Sora — OpenAI’s text-to-video model – YouTube
OpenAI’s other products like ChatGPT and text-to-image generator DALL.E3 share re-captioning technique which generates highly descriptive captions for visual training data. The same technology allows Sora to follow user prompts consistently. Sora is also able to generate video from existing images, detailing the minute features on the image. It can also extend the existing video or fill in the missing frames to make the video more coherent.
Key Features as compared to other text-to-video models:
Sora is not the first one to launch text-to-video generative AI tool. Google launched lumiere powered by new diffusion model Space-Time-U-Net/ STUNet. Meta launched Emu Video which works on two-step process, first it converts text to image and then from image to video. Phenaki video developed Mask GIT to produce text-guided videos in PyTorch, it can generate videos up to two minutes. One of the most famous text-to-videos by Stable Video Diffusion by Stability AI.
- Video Length: Sora can generate videos up to a minute long, significantly exceeding the capabilities of earlier models, which were often limited to mere seconds. Lumiere’s videos are around 5 seconds long, while Sora makes videos up to 60 seconds.
- Video Quality: Sora can generate videos with a resolution of up to 1920 × 1080 pixels, and in a variety of aspect ratios, while Lumiere is limited to 512 × 512 pixels.
- Complexity: Sora excels at crafting intricate scenes with multiple characters, diverse emotions, and realistic movements – aspects that previous models struggled with.
- Prompt Fidelity: Unlike some earlier models that seemed to generate results loosely based on prompts, Sora demonstrates a remarkable ability to accurately translate user descriptions into visuals.
Lumiere is unable to create multiple-shot videos, while Sora is able to do so. Like other models, Sora is also said to be able to do video-editing jobs like video-editing, video-animation, video-mixing, and video-extension.
Though Sora is currently available to red teamers to assess important safety features and to better understand potential responses to prompts. It is also available to visual artists, designers and film makers to get feedback to make it more creative and realistic for different applications.
Risks and Challenges
While Sora’s potential is undeniable, it’s crucial to acknowledge the inherent risks and challenges associated with such powerful technology:
- Misinformation and Deepfakes: The ability to generate realistic videos based on text prompts raises concerns about the potential for creating and disseminating fake news and deepfakes, posing threats to public trust and discourse.
- Bias and Discrimination: Like other AI models, Sora’s outputs are influenced by the data it’s trained on. If not carefully monitored and mitigated, these biases can lead to discriminatory or offensive content generation.
- Ethical Considerations: The widespread adoption of text-to-video technology raises ethical questions surrounding ownership, copyright, and the potential for misuse in various contexts.
These challenges highlight the critical need for responsible development, deployment, and regulation of this technology.
Real-World Applications
Despite the concerns, Sora holds immense potential for various real-world applications, including:
- Storytelling: Sora can help filmmakers, animators and storytellers visualize their ideas in a fast and efficient way, allowing them to explore their ideas more creatively and quickly prototype them.
- Training: Text-to-video (T2V) content generation can revolutionize education and training by creating engaging and interactive content that can be tailored to different learning styles.
- Marketing: Sora can help you create product demos and explainer videos, as well as personalized marketing materials that are tailored to your target audience and contexts.
Sora is a game-changer in text to video AI. While it’s important to understand the risks and challenges involved, using it creatively can open up huge opportunities across multiple industries and shape the future of content creation and communication.
For more information on Sora, you can visit: Sora (openai.com)