Background
In recent times, AI-generated images have captivated the public’s imagination, with platforms like OpenAI’s ChatGPT-4o enabling users to create visuals in distinctive styles, such as those reminiscent of Studio Ghibli. This phenomenon, often termed “Ghiblification,” has sparked both admiration and ethical debates regarding the use of AI in creative processes.
Despite the impressive capabilities of these AI systems, many users have noticed that AI-generated images often possess certain “weird” or unnatural characteristics. But what causes these peculiarities? There are indeed interesting points that are not often discussed in AI-generated art controversies. Let me give you the latest example.
(Source: https://variety.com/2025/digital/news/openai-ceo-chatgpt-studio-ghibli-ai-images-1236349141/)
Introduction: When AI Art Falls Short
The image you’re looking at depicts what appears to be an anime-style scene with three characters on a street, seemingly attempting to recreate the famous “distracted boyfriend” meme in a Studio Ghibli aesthetic. While at first glance it might seem impressive to some, artists and animation professionals would quickly spot numerous technical flaws:
This example that was supposedly created would be embarrassingly low quality to actual artists. First, unlike in the meme, the man’s gaze isn’t directed at the woman in red’s buttocks, and his expression and posture also differ from the original meme. The right woman’s expression, gaze, and posture are all ambiguous. The hair colors should be portrayed differently to give the characters more personality. The eye level in the background is drawn incorrectly too high. If someone brought artwork of this quality to an animation studio, their boss would tear it up. Moreover, it’s frustrating that the internet will now be flooded with these low-quality, seemingly plausible Ghibli images.
This critique reveals more than just aesthetic preferences—it highlights fundamental technical limitations in today’s AI image generation systems. Let’s explore why these issues persist despite rapid advances in the field.
The Technical Foundation: Beyond Simple Diffusion
Most current text-to-image generators rely on latent diffusion models (LDMs), but understanding their limitations requires looking deeper into their architecture and training methodology.
Latent Space Representations and Their Limitations
At their core, LDMs operate by compressing images into a lower-dimensional “latent space” where each point represents essential features rather than raw pixels. This compression is performed by an encoder network, typically a variational autoencoder (VAE). The diffusion process occurs in this compressed space, making it computationally efficient compared to pixel-space diffusion.
The technical limitation begins here: the compression into latent space inevitably loses information. While this tradeoff is necessary for computational efficiency, it means that fine-grained details about spatial relationships, lighting consistency, and multi-object interactions are often simplified or lost entirely.
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During training, the model learns to reverse a gradual noising process, effectively learning to predict the direction toward less noisy data. However, this process is fundamentally based on learning correlations rather than understanding causal relationships or physical constraints.
The Probabilistic Nature of Generation
Text-to-image models generate content by sampling from learned probability distributions conditioned on text inputs. Each pixel’s value is effectively a probabilistic guess based on patterns observed during training. This leads to a fundamental challenge: the model has no explicit understanding of physical reality, 3D space, or natural laws.
When generating the spatial relationship between characters (as in our example image), the model isn’t calculating sight lines, body mechanics, or social dynamics—it’s sampling from distributions of pixel patterns that were statistically associated with similar prompts in the training data.
The Multi-Entity Problem in Image Generation
One of the most persistent challenges in AI image generation is coherently representing multiple entities interacting in a scene—precisely what makes our example image problematic.
Attention Mechanism Limitations
While transformer-based architectures with cross-attention mechanisms allow models to associate text tokens with image regions, these associations remain fundamentally probabilistic and lack explicit structural constraints.
When given a prompt like “a man looking at a woman in red while his girlfriend looks angry,” the model must:
- Generate three separate human figures
- Position them appropriately relative to each other
- Align their gaze directions consistently
- Express appropriate emotions through facial features
- Maintain consistent lighting and perspective across all elements
Each of these requirements introduces compounding opportunities for error, with no underlying 3D scene graph or physical model to enforce consistency. The attention mechanisms provide loose associations but not strict geometric or physical constraints.
The Compositional Gap
Recent research from Stanford and MIT highlights what researchers call the “compositional gap”—the inability of current models to reliably produce novel combinations of concepts and relationships not explicitly seen during training. This gap is particularly evident in scenes requiring specific spatial or interactive relationships between multiple entities.
While models have seen millions of human faces and bodies, they’ve seen a much smaller set of multi-person interactions from consistent viewpoints. They’ve likely seen even fewer examples of the specific “distracted boyfriend” scenario in anime style—explaining why the generated image fails to capture the essence of the meme properly.
The Data Problem: Quality vs. Quantity
The Curse of Web-Scale Training
Most state-of-the-art image generation models are trained on vast datasets of image-text pairs scraped from the internet. While this approach provides scale, it introduces significant noise:
- Inaccurate or generic text descriptions that don’t capture nuanced visual details
- Biased representations of concepts
- Low-quality images with poor composition, lighting, or perspective
- Limited examples of complex multi-entity scenes with specific interaction patterns
The models must learn from this noisy data, extracting patterns that may not actually reflect artistic or physical reality. For example, when learning “anime style,” the model sees a vast array of anime images of varying quality and style, from professional Studio Ghibli productions to amateur fan art.
The Animation Knowledge Gap
Traditional animation studios like Ghibli employ rigorous principles around character design, movement physics, emotional expression, and spatial consistency. These principles aren’t explicitly labeled in training data—they must be implicitly extracted from examples.
The subtle principles of animation that make characters feel alive—anticipation, follow-through, arcs of motion, and consistent character design—aren’t directly encoded in static training images. Without explicit guidance on these principles, models struggle to generate images that follow them consistently.
Recent Advances: Closing the Gap
Despite these challenges, several recent technical innovations have begun addressing some of these limitations:
3D-Aware Generation
Recent models like Google’s Imagen 3D and OpenAI’s DALL-E 3 have incorporated 3D awareness into the generation process. By implicitly or explicitly modeling 3D structure during generation, these models can maintain more consistent spatial relationships, though significant challenges remain in complex scenes.
Compositional Generation Approaches
Research teams at NVIDIA and Meta have recently published approaches for more compositional image generation, breaking scenes into component parts with explicit relationships before final rendering. For instance, NVIDIA’s EDGI generates explicit layout maps before rendering the final image, providing greater control over spatial relationships.
Control-Net and ControlLora Techniques
The development of ControlNet and similar techniques has allowed more precise control over generated images through additional conditioning signals like pose estimation, depth maps, or segmentation masks. These approaches provide explicit constraints that help maintain spatial and interactive consistency.
The Future: Bridging Artistic Understanding and Technical Capability
Despite impressive advances, the gap between AI-generated art and professional human-created art persists, especially in domains requiring consistent character interaction, emotional expressiveness, and stylistic fidelity.
True advances will likely require:
- More structured training data with annotations for artistic principles and physical relationships
- Hybrid approaches combining neural generation with explicit 3D scene modeling
- Interactive refinement tools that allow artists to guide and correct AI-generated content
- Models that understand not just statistical patterns but causal relationships in scenes
Conclusion: The Art in Artificial Intelligence
The critique of our AI-generated Ghibli-style image reveals not just aesthetic disappointment but the fundamental disconnect between how current AI systems represent visual concepts and how human artists understand them.
While AI image generation will continue to improve, recognizing these technical limitations helps us understand why certain artifacts appear and informs approaches for addressing them. The gap between human and AI art isn’t just about computation power or dataset size—it’s about the fundamental difference between statistical pattern recognition and the causal, intentional understanding that human artists bring to their work.
What we’re witnessing is not the replacement of human artists but the evolution of new tools with specific capabilities and limitations. Understanding these technical boundaries helps us use these tools more effectively and appreciate the unique value of human artistic judgment.