Ex-Googlers’ AI Startup OpenArt Now Generates ‘Brain Rot’ Videos with a Single Click

AI-Generated “Brain Rot” Videos Take the Internet by Storm

The latest trend in online entertainment, AI-generated “brain rot” videos, captures the imagination of younger audiences with quirky characters like a sneaker-wearing shark and a ballerina with a cappuccino head.

OpenArt: A Startup Fueling the Trend

Founded in 2022 by former Google employees, OpenArt has quickly garnered around 3 million monthly active users, becoming a leading force in this emerging space.

Introducing the “One-Click Story” Feature

OpenArt recently unveiled its innovative “one-click story” feature, currently in open beta. This tool allows users to transform a simple sentence, script, or song into a captivating one-minute video. Whether for TikTok pleasantries or serious content like explainer videos for YouTube, this feature is poised to revolutionize digital storytelling, even in advertising.

Choose Your Template: Character Vlog, Music Video, or Explainer

With One-Click Story, users can select from three templates: Character Vlog, Music Video, or Explainer. For character vlogs, users upload an image and set a prompt. The software even understands song lyrics, creating animations that resonate with the themes, like illustrating blooming flowers in sync with the melody.

Edit and Refine Your Videos Effortlessly

Users can easily fine-tune their videos by revisiting the editor’s storyboard mode, adjusting prompts for a polished final product. With access to over 50 AI models, users can select tools like DALLE-3, GPT, Imagen, Flux Kontext, and Stable Diffusion to enhance their creations.

OpenArt-One-Click Story
Image Credits:OpenArt

Lowering Barriers for Aspiring AI Creators

The intent behind this feature is to simplify the path for budding AI creators, a medium that continues to thrive despite ongoing debates about its ethical implications.

Navigating Ethical Concerns in AI Content Creation

While these tools accelerate content generation with original characters and narratives, they raise numerous ethical questions, including issues of style imitation, intellectual property rights, and the potential for misinformation.

Intellectual Property Risks and Legal Considerations

During testing, concerns arose regarding the Character Vlog option, which could inadvertently incorporate copyrighted characters like Pikachu and SpongeBob, risking intellectual property (IP) violations. Notably, in June, Disney and Universal took legal action against AI firm Midjourney over AI-generated images.

Content creators should be cautious—if their videos infringe on copyright, they risk removal from social media platforms and potential legal repercussions.

OpenArt’s Commitment to Intellectual Property Compliance

Coco Mao, co-founder and CEO of OpenArt, emphasized their cautious approach to IP issues. “When you upload some IP characters, our models reject them by default,” she explained. However, the system may inadvertently allow some through.

Mao also expressed interest in negotiating licensing deals with major IP holders to better navigate this landscape.

OpenArt-Character Consistency
Image Credits:OpenArt

Ensuring Character Consistency: A Unique Selling Point

OpenArt differentiates itself by ensuring character consistency throughout videos. Unlike typical video models relying on standalone clips, OpenArt maintains cohesive narratives, enhancing audience immersion.

Future Plans: Enhanced Features and Mobile Potential

Moving forward, the company aims to develop the one-click feature further, allowing for dialogue between two different characters. A mobile app is also on the horizon.

Pricing Plans and Growth Trajectory

OpenArt operates on a credit-based system with four subscription plans: the basic plan at $14 per month for 4,000 credits (covering up to four One-Click stories), the advanced plan at $30 for 12,000 credits, the Infinite plan at $56 for 24,000 credits, and a team plan at $35 per member per month.

To date, OpenArt has raised $5 million from Basis Set Ventures and DCM Ventures, achieving positive cash flow and aiming for an annual revenue exceeding $20 million.

Here are five FAQs based on the OpenArt AI startup that creates "brain rot" videos:

FAQ 1: What is OpenArt?

Answer: OpenArt is an AI startup founded by former Googlers that specializes in generating creative content, particularly videos, using advanced artificial intelligence. Their platform allows users to create engaging videos with minimal effort, often described as "brain rot" due to their captivating and addictive nature.

FAQ 2: How does OpenArt create videos?

Answer: OpenArt utilizes sophisticated algorithms and machine learning techniques to analyze trends and user preferences. By simply clicking a button, users can generate unique videos that blend visuals, sound, and themes tailored to their tastes, making video creation quick and easy.

FAQ 3: What are "brain rot" videos?

Answer: "Brain rot" videos refer to highly engaging, often repetitive or overly stimulating content designed to capture and hold viewers’ attention. These videos are typically entertaining but may not provide substantial intellectual value, appealing more to emotions and quick entertainment.

FAQ 4: Is there a cost associated with using OpenArt?

Answer: OpenArt offers various pricing plans, including a free tier with limited features and premium subscriptions that provide access to more advanced options and tools. The specifics can vary, so checking their website for the latest pricing details is recommended.

FAQ 5: Can I use OpenArt for commercial purposes?

Answer: Depending on the terms of service, users may be able to use videos created with OpenArt for commercial purposes. It’s essential to review their licensing agreements and any restrictions before using the videos in commercial projects to ensure compliance.

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Unveiling Meta’s SAM 2: A New Open-Source Foundation Model for Real-Time Object Segmentation in Videos and Images

Revolutionizing Image Processing with SAM 2

In recent years, the field of artificial intelligence has made groundbreaking advancements in foundational AI for text processing, revolutionizing industries such as customer service and legal analysis. However, the realm of image processing has only begun to scratch the surface. The complexities of visual data and the challenges of training models to accurately interpret and analyze images have posed significant obstacles. As researchers delve deeper into foundational AI for images and videos, the future of image processing in AI holds promise for innovations in healthcare, autonomous vehicles, and beyond.

Unleashing the Power of SAM 2: Redefining Computer Vision

Object segmentation, a crucial task in computer vision that involves identifying specific pixels in an image corresponding to an object of interest, traditionally required specialized AI models, extensive infrastructure, and large amounts of annotated data. Last year, Meta introduced the Segment Anything Model (SAM), a revolutionary foundation AI model that streamlines image segmentation by allowing users to segment images with a simple prompt, reducing the need for specialized expertise and extensive computing resources, thus making image segmentation more accessible.

Now, Meta is elevating this innovation with SAM 2, a new iteration that not only enhances SAM’s existing image segmentation capabilities but also extends them to video processing. SAM 2 has the ability to segment any object in both images and videos, even those it hasn’t encountered before, marking a significant leap forward in the realm of computer vision and image processing, providing a versatile and powerful tool for analyzing visual content. This article explores the exciting advancements of SAM 2 and its potential to redefine the field of computer vision.

Unveiling the Cutting-Edge SAM 2: From Image to Video Segmentation

SAM 2 is designed to deliver real-time, promptable object segmentation for both images and videos, building on the foundation laid by SAM. SAM 2 introduces a memory mechanism for video processing, enabling it to track information from previous frames, ensuring consistent object segmentation despite changes in motion, lighting, or occlusion. Trained on the newly developed SA-V dataset, SAM 2 features over 600,000 masklet annotations on 51,000 videos from 47 countries, enhancing its accuracy in real-world video segmentation.

Exploring the Potential Applications of SAM 2

SAM 2’s capabilities in real-time, promptable object segmentation for images and videos open up a plethora of innovative applications across various fields, including healthcare diagnostics, autonomous vehicles, interactive media and entertainment, environmental monitoring, and retail and e-commerce. The versatility and accuracy of SAM 2 make it a game-changer in industries that rely on precise visual analysis and object segmentation.

Overcoming Challenges and Paving the Way for Future Enhancements

While SAM 2 boasts impressive performance in image and video segmentation, it does have limitations when handling complex scenes or fast-moving objects. Addressing these challenges through practical solutions and future enhancements will further enhance SAM 2’s capabilities and drive innovation in the field of computer vision.

In Conclusion

SAM 2 represents a significant leap forward in real-time object segmentation for images and videos, offering a powerful and accessible tool for a wide range of applications. By extending its capabilities to dynamic video content and continuously improving its functionality, SAM 2 is set to transform industries and push the boundaries of what is possible in computer vision and beyond.

  1. What is SAM 2 and how is it different from the original SAM model?
    SAM 2 stands for Semantic Association Model, which is a new open-source foundation model for real-time object segmentation in videos and images developed by Meta. It builds upon the original SAM model by incorporating more advanced features and capabilities for improved accuracy and efficiency.

  2. How does SAM 2 achieve real-time object segmentation in videos and images?
    SAM 2 utilizes cutting-edge deep learning techniques and algorithms to analyze and identify objects within videos and images in real-time. By processing each frame individually and making predictions based on contextual information, SAM 2 is able to accurately segment objects with minimal delay.

  3. Can SAM 2 be used for real-time object tracking as well?
    Yes, SAM 2 has the ability to not only segment objects in real-time but also track them as they move within a video or image. This feature is especially useful for applications such as surveillance, object recognition, and augmented reality.

  4. Is SAM 2 compatible with any specific programming languages or frameworks?
    SAM 2 is built on the PyTorch framework and is compatible with Python, making it easy to integrate into existing workflows and applications. Additionally, Meta provides comprehensive documentation and support for developers looking to implement SAM 2 in their projects.

  5. How can I access and use SAM 2 for my own projects?
    SAM 2 is available as an open-source model on Meta’s GitHub repository, allowing developers to download and use it for free. By following the instructions provided in the repository, users can easily set up and deploy SAM 2 for object segmentation and tracking in their own applications.

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Identifying Deepfake Videos: Tips for Spotting Them Like a Fact-Checker

Are you aware of the rising prevalence of deepfakes online? Deepfakes are digitally crafted videos where an individual’s likeness is replaced with someone else’s, posing a significant threat by spreading misinformation worldwide. It is crucial for individuals to be able to differentiate between genuine content and deceptive deepfakes to combat this growing issue.

Not everyone has access to advanced software for identifying deepfake videos. However, fact-checkers follow specific strategies to authenticate videos, and you can adopt these techniques to protect yourself from falling victim to fabricated content.

1. Analyze the Context:
It is essential to scrutinize the context in which a video is presented. Check the background story, setting, and events portrayed in the video against known facts to detect inconsistencies that may indicate a deepfake. For instance, a deepfake video of Ukrainian President Volodymyr Zelensky urging troops to surrender to Russian forces surfaced on social media, but closer examination revealed contextual clues that exposed its inauthenticity.

2. Verify the Source:
Always check the source of a video to ensure its credibility. Hackers often use videos to deploy cyberattacks, with the rise of deepfake videos contributing to the threat. Videos from trustworthy sources are less likely to be deepfakes, so cross-check them with reputable news outlets or official websites for validation.

3. Look for Inconsistencies in Facial Expressions:
Deepfakes may exhibit inconsistencies in facial expressions, such as unnatural blinking, lip sync errors, and exaggerated emotions. Pay attention to these details to uncover signs of manipulation in the video.

4. Analyze the Audio:
Audio quality and characteristics can also help detect deepfakes. Deepfake voices may sound robotic or lack natural emotional inflections, indicating artificial manipulation. Changes in background noise or sound quality within the video may suggest tampering.

5. Investigate Lighting and Shadows:
Observing the lighting and shadows in a video can reveal its authenticity. Deepfake technology often struggles to replicate real-world lighting effects accurately. Anomalies in lighting or irregular shadows can indicate a video has been doctored.

6. Check for Emotional Manipulation:
Deepfakes are designed to evoke emotional responses and manipulate viewers. Assess whether the video aims to trigger strong emotions like fear or shock, and cross-verify the content with reputable sources to avoid falling for emotionally charged fabrication.

7. Leverage Deepfake Detection Tools:
As deepfakes become more sophisticated, utilizing detection tools that employ AI and machine learning can aid in identifying fake videos. Microsoft’s Video Authenticator and other technologies are continually developed to combat evolving deepfake threats.

By staying vigilant and utilizing these strategies, you can effectively detect and protect yourself from deceptive deepfake videos circulating online. Remember to always verify the source and remain informed to safeguard the truth in the age of fake media.





How to Identify Deepfake Videos FAQs

How to Identify Deepfake Videos FAQs

1. What is a deepfake video?

A deepfake video is a manipulated video created using artificial intelligence techniques, which makes it appear as though someone is saying or doing something they never did in reality.

2. How can I spot a deepfake video?

To identify a deepfake video, look for these signs:

  • Inconsistencies in lip-syncing or facial expressions
  • Unnatural lighting or shadows
  • Blurry or distorted areas in the video

3. Can deepfake videos be used to spread misinformation?

Yes, deepfake videos can be used to spread misinformation by manipulating footage of well-known figures or creating fake news stories. Fact-checkers play a crucial role in debunking such content.

4. How do fact-checkers verify the authenticity of videos?

Fact-checkers use various techniques to verify the authenticity of videos, such as:

  • Reverse image searches to identify original sources
  • Consulting experts in facial recognition and video analysis
  • Comparing metadata and timestamps of the video

5. What actions can I take if I come across a deepfake video?

If you come across a deepfake video, you can report it to the platform hosting the video, share it with fact-checkers, and educate others about the dangers of misinformation spread through deepfake technology.



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