The generative revolution in graphical content creation

The landscape of content generation is transforming. The era of tedious production cycles and narrow customization options is giving way to a new dawn, owing to the emergence of foundational models in the realm of Computer Vision. This progress, marked by the leaps of Generative AI, is carving out an exciting new chapter for graphics in marketing and advertising. Such innovations herald a future where content can be exquisitely tailored to customer segments and market demographics, all while upholding brand identity, respecting cultural nuances and ethnic diversity.

This technology is unlocking the potential for scaling up content production, enabling widespread reach that doesn’t sacrifice the depth of personalization. This discussion delves into Generative AI’s transformative impact, highlighting how vision-language models (VLMs) redefine graphic content creation for marketing and advertising across different sectors.

The Computer Vision team at AI Client Services, Fractal has been trying to understand and address the hurdles encountered by the graphic design industry, which serves various clients with their advertising and marketing initiatives. They recognized the obstacles inherent in consistently producing novel content. The CV team realized that the mere generation of images from text descriptions, while useful, falls short of meeting the expanding needs of this vibrant sector. The field calls for integrating various modalities to craft the ideal visual narrative that meets all the expected graphic design criteria.

We developed an accelerator, titled ImagineAI to expedite tasks in multimodality – especially, vision-based tasks in graphical content generation processes. Facilitating automation, which was forever considered difficult in this domain, the CV team incorporated a guideline checker within their accelerator to ensure both existing and newly created content adhere to the necessary standards. The accelerator streamlines the process of creating and moderating graphical content, often reducing it to a single step. It rigorously complies with not-safe-for-work (NSFW) content restrictions and adheres to brand-specific guidelines.

Unleashing creativity: The rise of Generative AI

Generative AI, a subfield of AI focused on creating new data, as well as carrying out generative tasks – has become a game-changer in graphical content creation. Models like Stable Diffusion, Midjourney, and Dall-e represent the forefront of generative AI progress in the domain of Computer Vision. These AI wonders leverage the power of prompt engineering, allowing users to translate simple ideas into stunning visuals. By strategically crafting prompts, creators can guide the AI to generate images that match their imagination. This empowers businesses to break free from creative roadblocks and explore an infinite canvas of possibilities.

Transforming textual descriptions into visual imagery enables realizing creative concepts into tangible visuals. Yet, this process alone might not meet all the requirements for creating marketing collaterals like – developing fresh advertisements for product introductions. The intricacies in graphical content creation involve integrating various elements, such as engaging headlines or taglines, putting up essential product information at appropriate places, and imagery often serving as the backdrop to highlight the featured product. Additionally, strategically positioned QR codes are incorporated to facilitate immediate purchases. Overall, the layout of an advertisement must align with the brand’s distinctive style, including specific formats and colour schemes.

In a conventional design studio set-up, the above components, typically established over years of practice as the brand’s digital assets by the team of designers, are developed and subsequently utilized as foundational elements for consistent content production. Crafting a poster or flyer for an event or a product launch remained a meticulous procedure that typically involved multiple briefings and revisions. Through this iterative process, the designer attempts to achieve an impeccable design that resonates with various critical elements, including the brand’s essence, the intended demographic, standout features of the product, a well-structured visual flow, the core message of the product, adherence to different media channels and sensitivity to cultural and demographic nuances.

The emergence of vision-language models with Generative AI has revolutionized the landscape of content generation, automating the aforementioned tasks as an end-to-end process – once deemed unattainable. The inherent duality in vision-language foundation models – is that they are adept in tasks related to understanding graphical content, such as the strategic placement of text for taglines, distinguishing different types of content within an asset, be it text, QR codes, or logos, and selecting the appropriate template from a brand-defined collection. They also excel in generating new content from the existing content, that include designing flyer backgrounds that accentuate the product in the intended environment, formulating catchy product taglines, and even composing brief inspirational phrases for marketing campaigns. These models can also create fresh visual representations of products through sophisticated neural rendering techniques, all while maintaining specific lighting conditions and image compositional accuracy.

But the magic doesn’t stop there. These models in Generative AI also enable hyper-personalization. VLMs merge computer vision and language processing to understand the nuances of both image and text. Imagine an AI system that can understand a user persona and create visuals of a vacation that connects with the individual emotionally, as well as proposes a pocket-friendly budget to avail the vacation. All these are possible with just a properly engineered prompt – which can be a combination of text and visual input that generate graphical content matching the requirement – as just a one-step process. This level of customization allows brands to speak directly to their customers, fostering deeper connections and boosting engagement.

There’s also immense potential for AI-powered content creation in the realm of influencer marketing. Imagine automatically generating visuals seamlessly integrating an influencer’s style with the brand’s messaging. This ensures brand consistency while allowing influencers to express their creativity. Additionally, AI can analyze influencer content to identify high-performing elements and replicate them in future campaigns, further optimizing engagement.

Putting vision-language models to work: real-world applications

The impact of vision-language models on Generative AI in industries:

• Retail: Imagine generating high-quality product images for every new item in your inventory, automatically adapting them to different contexts (clothing on a model in various sizes, furniture in different room settings). VLMs can churn out product images faster than ever before, catering to diverse customer segments with personalized visuals. This allows retailers to test different product presentations and optimize conversion rates.

• CPG (consumer packaged goods): Launching marketing campaigns often requires a flurry of content creation for various channels (social media, print ads, website banners). Generative AI streamlines this process, allowing CPG companies to quickly generate visuals for their latest offerings, with options for different demographics and cultural contexts. This accelerates time-to-market and maximizes campaign effectiveness. For instance, a company launching a new cereal brand can use AI to generate social media posts with images featuring families from diverse backgrounds enjoying breakfast with the cereal.

• BFSI (banking, financial services, and insurance): VLMs can analyze customer data to identify distinct segments with unique needs and preferences. Computer vision then takes over, crafting segment-specific content (e.g., investment advice visuals for young professionals, retirement planning infographics for seniors) that resonates with each target audience. This reduces manual workload by a significant margin, empowering marketers to redirect focus on strategy rather than execution.

• Personalized social media experiences: Imagine social media platforms leveraging AI to curate personalized feeds. VLMs could analyze user preferences and past interactions to generate feeds populated with content that aligns with their interests. This could include automatically generating images for travel recommendations based on a user’s saved locations or creating personalized product suggestions based on their browsing history.

• Real estate marketing: Selling a property often hinges on showcasing its potential. Generative AI-powered virtual staging can create realistic images of a vacant property furnished and decorated, allowing potential buyers to envision themselves living in the space. Additionally, a VLM finetuned on this small data can generate location-specific neighborhood visuals, highlighting nearby amenities and attractions.

• E-learning and training materials: Static textbooks and lectures are a thing of the past. VLMs can create dynamic and engaging e-learning materials by generating visuals that complement the written content. Picture interactive anatomy lessons with AI-generated 3D models or historical simulations brought to life with realistic images of past events.

Some examples of flyers generated by Fractal’s Content Generation platform ImagineAI are provided here. It is to be noted that the flyers were generated using a single prompt with pre-defined variables.

Some more examples of flyers generated with translated and transliterated content in English.

The art of making creative adaptations with Generative AI

Creative copying, the art of preserving core elements of a creative asset while adapting it for different audiences, finds new life with Generative AI. The core idea is to take a high-performing creative which could be a flyer or a social media post featuring a product image and positive customer testimonials. ImagineAI team has developed a Gen AI based multimodal model that can create variations of the post, swapping out the background or model to reflect a new demographic while keeping the core message and product image intact. This empowers brands to maximize the ROI of their existing content library, reaching new audiences without starting from scratch.

(Source: images generated at Freepik | Create great designs, faster)

Beyond the image: ethical considerations in brand moderation

As Generative AI steps into the content creation landscape, ethical considerations become paramount. Foundation models in vision-language trained on vast datasets can perpetuate biases if not carefully monitored. ImagineAI’s brand moderation utilizes generative AI to ensure content adheres to brand guidelines, eliminates racial or ethnic bias, and effectively moderates influencer marketing initiatives. This fosters brand trust and protects businesses from reputational harm.

The technical details involve leveraging anomaly detection algorithms to identify content that deviates from pre-defined parameters (e.g., brand logos, color palettes). Sentiment analysis can detect potentially offensive language or imagery. For instance, a VLM can analyze influencer posts to ensure they comply with brand safety guidelines and avoid promoting harmful stereotypes. By continuously monitoring and refining these algorithms, brands can ensure their content remains ethical and inclusive.

In the realm of content moderation for graphic designs, ImagineAI can respond to visual inquiries, such as identifying the color of a dress within an image, or discern, based on ingrained brand guidelines, if the image content is appropriate for the intended audience. They can verify that a produced poster is in harmony with the overarching marketing strategy, encompassing the theme of the ad campaign, its storyline, and the distribution channels. To ensure compliance, these models can generate a report detailing any breached guidelines and provide recommendations on necessary adjustments to meet those standards.

Furthermore, it’s crucial to ensure transparency in AI-generated content. Disclosing the use of AI and providing clear information about the training data employed fosters trust with consumers. This transparency allows audiences to make informed decisions about how they interact with the content.

The multimodal future: beyond text and image

Content creation is embracing multimodality, a future where text, image, video, and even new modalities like audio and touch are seamlessly integrated. Imagine interactive product demonstrations with AI-generated visuals and voice-over narration that dynamically adjusts based on user preferences (e.g., highlighting features relevant to experienced hikers for a hiking boot demonstration). This immersive experience allows consumers to engage with products on a deeper level, fostering informed purchase decisions.

The possibilities extend beyond product demonstrations. Picture educational content that combines text, animation, and interactive elements, catering to different learning styles. Generative AI could personalize the learning experience by adjusting the difficulty level, pace, and content based on the user’s progress and understanding. This personalized approach to content creation has the potential to revolutionize education and training across various fields. However, ethical considerations remain paramount in the multimodal future. Ensuring user data remains secure and using it responsibly will be critical for building trust and fostering widespread adoption of multimodal content experiences.

Conclusion: the generative collaboration

Generative models empower creators to push creative boundaries, while foundation models streamline workflows and enable hyper-personalization. As Generative AI evolves, we are now able to witness even more sophisticated models capable of crafting content that is not only visually stunning but also emotionally resonant and culturally relevant. However, navigating the ethical considerations and ensuring responsible development will be crucial for maximizing the positive impact of Generative AI in the content creation landscape. AI is not here to replace human creativity; it’s here to augment it, empowering us to tell stories and share ideas in ways never before imagined. The future of content creation is undeniably multimodal, immersive, and driven by the power of human-AI collaboration.