Artificial intelligence struggles to create realistic images of hands because of the intricate complexity and variability of human hands, insufficient training data, and the uncanny valley effect where AI-generated hands appear almost real but not quite accurate enough.
Artificial intelligence has made incredible progress in generating stunningly realistic and creative images from simple text prompts. Sophisticated AI art tools like DALL-E 2, Midjourney, and Stable Diffusion can conjure up impressively detailed portraits, landscapes, and surreal scenes with just a few words.
However, take a closer look at images containing hands generated by these AI systems, and things start to fall apart. Extra, missing, or melted fingers and contorted wrists abound in AI art. The AI seems to have trouble accurately depicting one of the most important parts of the human body – our hands.
But why exactly do AI image generators struggle so much to create natural-looking hands compared to other parts of the body? What inherent challenges make hands so difficult for current AI to depict realistically?
The Intricate Complexity and Variability of Human Hands
One of the main reasons artificial intelligence struggles with hands is their intricate complexity compared to many other objects. Our hands are incredibly complex anatomical structures that allow humans to grasp, touch, feel, manipulate, create, communicate, and more.
This amazing flexibility and dexterity comes from the numerous bones, joints, tendons, muscles, and nerves that allow the 27 bones and 30 joints in each hand to move independently into countless positions and configurations.
Compare this to something visually simpler that AI tends to handle very well, like apples or oranges. These fruits are solid objects with a very familiar and consistent shape, limited variability, and fairly simple surface texture.
On the other hand, human hands can bend, contort, obscure themselves, and alter their appearance in many complex ways.
All this potential for variability throws off AI systems that are reliant on recognizing clear visual patterns from large datasets of training images.
When hands can look so different across various poses and gestures, it becomes much harder for the AI to identify the consistent patterns that define the core characteristics of an anatomically realistic hand.
To use an analogy, it’s like an apple will pretty much always look fundamentally like an “apple-shaped thing with red peel and maybe a leaf on top”.
But a hand could be a closed fist, spread open, grasping something, just the fingertips in view, obscuring the face, etc. The broad flexibility and visual variability make it a challenge for AI to determine what fundamental qualities make a hand still look like a hand in all these different positions.
Insufficient Training Data Focused on Hands
Another major factor is that current AI image generators have simply not been trained on datasets containing enough examples of hands in various positions to learn how to accurately depict them.
These machine learning models are trained by analyzing massive datasets of millions of images scraped from the internet, along with any accompanying text descriptions. But these datasets tend to contain far more pictures of faces than hands.
For example, the popular Flickr HQ dataset used to train many models contains 70,000 images focused on faces, but only 11,000 images prominently featuring hands.
Likewise, there are datasets with over 200,000 annotated images of celebrity faces with details like glasses, facial hair, age, etc.
But there is a lack of datasets with a similar number of hand images showing hands in diverse positions and annotated with details like finger positions, hand gestures, how the fingers curl, etc.
Without enough quality training data focused specifically on hands for the model to learn from, the AI cannot pick up on all the subtle patterns and relationships between the appearance and positioning of fingers, knuckles, palms, etc. needed to convincingly generate new hand images.
The training data currently available lacks sufficient details like:
- Each finger’s joint positions and angles
- Thumb placement relative to other fingers
- Overall hand function, posture, and anatomical structure
- How hands look from different viewpoints and in motion
So while current AI can paint a static frontal face with decent accuracy by matching patterns it’s seen before, its limited exposure to detailed hand images means hands end up as abstract masses of malformed digits when it tries to extrapolate.
The Uncanny Valley Effect of AI-Generated Hands
Another issue is that humans perceptually have very high standards and low tolerance when judging the visual accuracy of hands. In real life, we focus closely on people’s hands to understand nonverbal communication and intent through gestures and body language.
Even slight distortions like an extra finger or unnaturally bent wrists stand out immediately to our visual processing as “wrong” and creepy when we see them on AI-generated hands. This triggers an unsettling sensation of revulsion towards the image.
In psychology, this is known as the “uncanny valley” effect. It describes the phenomenon where as an artificial depiction of a human becomes increasingly realistic, small imperfections that diverge from expectations stand out as far more disturbing than in a more stylized or abstract depiction.
AI-generated hands often fall into this uncanny valley – they are too realistic in texture and detail to dismiss as a cartoon or abstract art, but also too anatomically inaccurate compared to our internal model of a normal human hand for our brains to accept it as a photo of a real person.
This effect likely occurs because humans have evolved to closely scrutinize hands for social clues, so we find any inaccuracies disturbing on a subconscious level compared to AI-generated faces or other body parts.
For hands to pass the visual Turing test in our minds, the AI needs to achieve even more precision than with faces to cross from the uncanny valley into true photorealism.
The Need for Deeper Structural Understanding Beyond Pattern Recognition
Finally, artificial intelligence struggles with hands because it currently lacks the deeper structural understanding of hands that allows humans to both recognize and draw them.
Mostly, AI image generators just copy patterns of pixels that they’ve learned correlate to particular words like “hand” without actually modeling the 3D structure of hands. In contrast, human artists simplify the complex form of hands down to basic shapes before constructing the details.
For example, an artist learns to first roughly sketch out the palm as a square block, the fingers as rectangular forms, etc. This simplification forms the scaffolding to then layer on realistic textures, shading, proportions, and perspectives of hands. Humans build our understanding of hands from seeing, touching, and using our own hands in the 3D world.
But AI systems only interpret hands from large datasets of 2D images with different appearances but no functional information. The AI has no innate knowledge of how hands truly operate, articulate, and exist in 3D space like humans do.
Without this structural comprehension, the AI cannot logically reconstruct hands the way a person naturally can. Like an alien seeing only photos, it lacks deeper insight into the relationship between finger joints, the role of knuckles, and other engineering subtleties of organic hands. So while it tries copying hand-like pixels from images, the result fails to match human expectations.
Advanced human artists can draw hands in creative new poses they haven’t directly seen before by intuitively adapting their mental model of hand anatomy. But for AI, every new hand prompt forces it to hallucinate details from scratch based only on fuzzy pattern associations, leading to distorted results.
Potential Solutions for the AI Hand Problem
There are several promising directions that could help artificial intelligence overcome its persistent struggle to generate natural-looking images of hands:
- Train models on larger datasets with more images of hands: Simply providing more hand photo references during training would expose the AI to more examples to learn from. Images with hands as the main focus labeled with poses and gestures would be especially helpful.
- Annotate hands in datasets to explain positioning/function: Detailing things like finger and joint angles, contact points, and functional relationships in training data can teach the AI hands’ mechanical essence.
- Use 3D hand models as training data: Augmenting 2D photos with 3D hand models during training may improve the AI’s spatial understanding.
- Employ human feedback to refine generated hands: Having people critique and fix AI output over many iterations can fine-tune the model based on human preferences.
- Develop better underlying hand structure models: Creating an articulated 3D hand model to guide image generation based on anatomical principles could make the output more structurally coherent.
With enough quality training data, smart training techniques, and innovations to the underlying architecture powering AI image generators, artificial intelligence will likely eventually match human artistic ability when it comes to depicting our most versatile appendage.
But for now, the persistent glitchiness of AI-generated hands reveals meaningful insights about the complexity of human anatomy and the limitations of current computer vision compared to the intuitive flexibilities of the human mind.
While AI art still falters at this one task, the hand hurdle arguably demonstrates how aspects of human visual intelligence continue to reign supreme over machines when it comes to our intuitive comprehension of the world around us.
The hand problem shows that while artificial intelligence has come a long way, it still does not perceive reality quite the same way biologically evolved minds do. There are still advantages and qualities unique to organic cognition that computer algorithms have yet to master.