Beyond Ordinary shows how to  Make Content Shine with AI, Digital Art Photos, and Avatars. It’s the best way to upgrade your book, blog, or video with an artistic rendition of the photos. The video below demonstrates the process. The avatar was created with the option to have different voices. Amazingly, it’s all done with Artificial intelligence (AI). We can now create mind-blowing visual assets using artificial intelligence with just a few clicks and prompts. Visual AI platforms now allow one to make all forms of AI visual art, from high-quality animated videos to charcoal sketches. We can now use AI To create  Videos, Images, Logos, Image Backgrounds, Headshots, Talking  Avatars, Cartoons, and more, with no artistic skill required.

In the previous blog, I demonstrated the benefits of converting photos to watercolours. I uploaded that blog to Lumen5, and using AI, we created a speaking Avatar. It’s early days, and I’m working to improve the avatar—but it’s a good attempt for a start.

The script created for the Avatar (below) was from https://roguesinparadise.com/rogues-becomes-a-work-of-art/

Avatar Script: “Discover the vibrant culture of Barbados in technicolor. The book captures  Barbados’ journey from a British colony to a republic. It blends history with the wit of the Bajan people. Photos are transformed into digital watercolors. This artistic choice enhances the book’s whimsical and cultural themes. Watercolors evoke warmth and nostalgia. They beautifully reflect Barbados’ landscapes, people, and history. Join the journey. Download chapters and get invited to the launch at https://sample.RoguesinParadise.com. “

The script is a pretty good interpretation of relevant text in my blog.

I then created a short video showing samples of the watercolor art transformed by the original photos.

Next I married the two videos using Screenflow.  As in the example below.

Music can be added by lumen 5 or Screenflow.

VIDEO EXAMPLE- Make Content Shine with AI

Note AI created the Avatar- there are several voices to choose from. The video script was derived from the blog. The example video was created with screen flow- The images were transferred from a photo by Befunky- with help. The example video was uploaded to Lumen5 for integration with the AI talking Avatar,

How AI Creates Talking Avatars

AI-generated talking avatars combine multiple technologies to bring scripts to life. Here’s how the process works:

  1. Text-to-Speech (TTS): AI converts your script into speech, using customizable voices to suit your tone and style.
  2. Avatar Creation: A pre-designed digital character is selected and tailored with customizable features like facial expressions and clothing.
  3. Lip Syncing & Animation: AI syncs the avatar’s lip movements and gestures to match the audio, adding natural movements like eye blinks and head nods for realism.
  4. Background Integration: The avatar is placed over customizable backgrounds, allowing for branded or themed settings.
  5. Video Editing & Rendering: Final touches, such as music, text overlays, and transitions, are added before exporting a polished video.

This process is powered by deep learning and animation frameworks, creating engaging content with lifelike avatars in just a few steps.

Ethical Issues with AI

I asked Chat GPT if they were concered about the ethics of replacing artist and creative people with AI- They replied as follows:

The use of AI-generated graphics and videos raises critical ethical considerations. Still, it does not necessarily spell the end for graphic artists. Instead, it reshapes the creative landscape, prompting reflection on fairness, originality, and labor dynamics. Here are the key points to consider:

Ethical Concerns

  • Attribution and Ownership: AI often generates content trained on datasets sourced from existing artwork, sometimes without proper attribution. This raises questions about intellectual property and the ethical use of these resources.
  • Job Displacement: While AI can perform tasks traditionally done by graphic artists, it doesn’t replace the creativity, cultural context, and nuanced skills of human designers. However, industries may prefer cost-effective AI solutions, leading to job insecurity for some artists.
  • Artistic Authenticity: Using AI tools might prompt debates about whether AI-generated visuals can be considered “real art” or just imitations.

Opportunities for Artists

  • Enhanced Creativity: Artists can use AI as a tool to expand their creative possibilities, automating repetitive tasks and focusing on more complex, meaningful work.
  • New Roles: The rise of AI creates roles like “AI artist,” where individuals guide and refine AI tools to achieve specific artistic visions.
  • Accessibility: AI tools democratize art creation, allowing non-artists to produce high-quality visuals. This expands creative access without entirely sidelining professionals.

How AI Creates Images (By Chat GPT)

AI creates images by leveraging deep learning models, specifically Generative Adversarial Networks (GANs) and diffusion models. Here’s how it works:

  1. Training on Data:
  2. The AI model is trained on vast datasets of images, learning patterns, textures, colors, and composition. For example, suppose the AI is trained to create watercolor art. In that case, it studies thousands of examples of watercolors to mimic their style.
  3. Generating Images:
  4. Once trained, the AI can generate new images based on prompts or instructions. The process involves:
    • Noise Reduction: Starting with random noise, the AI refines it step by step to create an image using learned patterns.
    • Text-to-Image Models: Advanced AI, like DALL·E, can interpret descriptive prompts (e.g., “a sunset over the Caribbean in watercolor style”) and turn them into images.
  5. Fine-Tuning:
  6. The AI can also modify existing photos, applying artistic styles like watercolor, sketch, or oil painting. Tools like BeFunky or Adobe Photoshop use AI algorithms to enhance or transform images.
  7. Creative Flexibility:
  8. AI doesn’t replicate exact images; instead, it generates unique interpretations based on what it has learned. This makes it a powerful tool for creating personalized, artistic visuals.

Here is what COHERE says:

AI image generation, particularly using Generative Adversarial Networks (GANs), involves a more complex process than simply modifying existing images. Here’s a simplified breakdown of how AI creates images:

    • Training Data: AI models, especially GANs, are trained on large datasets of images. These datasets can contain millions of images covering various subjects, styles, and visual elements. The diversity of the training data is crucial for the model to learn and understand the vast spectrum of visual possibilities.
    • Generator Network: A GAN has two main components: the generator and the discriminator. The generator network is responsible for creating new images. It takes random noise as input and uses this noise to generate an image. The noise acts as a seed, providing initial values that the generator transforms into an image.
    • Discriminator Network: The discriminator network evaluates the images generated by the generator. It has been trained to distinguish between real photos from the training dataset and the generated images. The discriminator provides feedback to the generator, indicating whether the generated image looks realistic.
    • Adversarial Training: The training process is adversarial, meaning the generator and discriminator are in a continuous loop of improvement. The generator aims to create images that fool the discriminator into thinking they are real. In contrast, the discriminator better distinguishes authentic images from generated ones. Over time, the generator learns to create increasingly realistic and diverse images through this iterative process.
    • Prompting and Conditioning: When generating images based on a specific prompt or condition, additional information is provided to the generator. This could be a text description, a style reference, or a partially completed image. The generator uses this information to guide the generation process, ensuring the output aligns with the prompt.
    • Refinement and Iteration: AI image generation often involves multiple iterations. The initial generated image might not perfectly match the prompt, so the model can be trained to refine and improve the image based on additional feedback or adjustments.

 

A Personal Observation on AI

As you can see each AI platform creates unique software to interrogate and remaster images. AI art is very stylized. They tend to look like AI-generated visuals. The algorithm looks at a huge database of images, photos, and graphics. This is a vulnerability as it uses other people’s creativity. The process involves complex mathematical operations, deep learning algorithms, and a thorough understanding of visual patterns and structures.

Next >>> AI Avatars or Visuals- The Debate Which is best

Related Blogs to follow:

    • Reimagine Creativity: Explore the Impact of AI in Digital Art and Storytelling
    • How AI is Changing the Way We Create Visual Content
    • The Ethical Side of AI in Art: What You Need to Know
    • Digital Artistry Made Easy: Insights on AI-Enhanced Creativity
    • AI Meets Art: Discover the Potential for Books, Blogs, and Videos
    • AI’s Evolving Role in Visual Publishing
    • Revolutionizing Visuals: AI’s Artistic Touch