Why AI art struggles with hands

4 Apr 202309:56

TLDRThe video discusses the peculiar difficulty AI art faces when generating human hands. Despite AI's ability to create complex and imaginative images, hands often appear distorted or unrealistic. This challenge stems from the AI's learning process, which relies on pattern recognition from vast datasets. Unlike humans who learn by observing and interacting with the world, AI is confined to analyzing images and descriptions, leading to a lack of understanding of how hands function. The issue is exacerbated by the scarcity and complexity of hand-related data in AI training datasets. The video also touches on the potential solutions, including increasing the diversity and quantity of training data and incorporating human feedback to refine AI's understanding and generation of images, particularly those involving hands.


  • ๐Ÿค– AI struggles with drawing hands because it lacks the nuanced understanding of human hands that comes from physical interaction and experience.
  • ๐ŸŽจ AI learns from patterns in images and descriptions, similar to humans, but without the ability to manipulate objects or see them from different angles.
  • ๐Ÿ‘€ AI's approach to creating images is based on pixel patterns and does not simplify complex structures like artists do, leading to unrealistic representations.
  • ๐Ÿ“š The quality and quantity of data available for training AI models play a significant role in their performance, with hands being less represented than other subjects.
  • ๐Ÿ” AI models have not been trained with detailed annotations about how hands function, which contributes to their inaccuracies.
  • ๐Ÿคนโ€โ™‚๏ธ The diversity of hand poses and actions compared to more static subjects like faces makes it challenging for AI to learn the 'rules' of hands.
  • ๐Ÿ‘ There's a low margin for error when it comes to hands; unlike other elements, being 'hand-like' isn't sufficient for viewers.
  • ๐Ÿข The AI's 'museum' analogy highlights its confinement to learning from a limited set of images and descriptions without real-world experience.
  • ๐Ÿงโ€โ™‚๏ธ Artists break down complex structures into basic forms, which AI does not do, leading to hands that look odd despite accurate light and texture.
  • ๐Ÿš€ Improvements in AI art models are being made, with newer versions showing progress in areas like hand representation.
  • ๐Ÿ‘ฅ Future advancements may involve more extensive data training and human feedback to fine-tune AI's understanding and generation of complex subjects like hands.

Q & A

  • Why do AI art models struggle with generating images of hands?

    -AI art models struggle with generating images of hands because they lack the pattern recognition and understanding of how hands function that humans possess. They are trained on images and descriptions, similar to being confined to a museum, and do not have the same ability to interact with and learn from the world as humans do.

  • How do humans learn to draw hands?

    -Humans learn to draw hands through pattern recognition, growing up and seeing many hands, and learning what they look like. Artists further simplify hands into basic forms, such as blocks for the palm and fingers, before adding style, texture, and detail.

  • What is the main difference between how AI learns and how humans learn to draw?

    -The main difference is that humans learn by interacting with the world and simplifying complex objects into basic forms, while AI learns from a limited set of images and descriptions without the ability to physically interact with objects or understand their functionality.

  • Why is it difficult for AI to generate images of hands accurately?

    -It is difficult for AI to generate images of hands accurately due to the scarcity and quality of hand data in training datasets, the complex and varied movements of hands, and the low margin for error in depicting hands correctly.

  • What are some of the reasons AI art models have trouble with hands, as identified by experts?

    -Experts identified three main reasons: the data size and quality available for training, the way hands act and move, and the low margin for error in the depiction of hands.

  • How does the AI's approach to generating images differ from an artist's approach?

    -The AI's approach involves recognizing patterns in pixels from the images it has been trained on, without understanding the underlying structure or function of what it's depicting. An artist, on the other hand, simplifies complex objects into basic forms and then adds details, having a structural understanding of the subject.

  • What is the significance of the 'museum' analogy used to describe the AI's learning environment?

    -The 'museum' analogy signifies that AI is confined to learning from a static and limited set of images and descriptions, much like a museum exhibit, without the ability to experience the world dynamically as humans do.

  • How might AI art models improve their generation of hands in the future?

    -AI art models could improve their generation of hands by being trained on larger and more diverse datasets, possibly incorporating human feedback to fine-tune the models and better understand the structure and function of hands.

  • What is the role of bias in the AI's ability to generate images?

    -Bias plays a role in that humans have certain expectations and a lower margin for error when it comes to generating images of hands. AI, lacking this bias, may generate 'hand-like' images that are not accurate enough for human standards.

  • How do AI art models currently handle the diversity and complexity of hands?

    -AI art models struggle with the diversity and complexity of hands, often generating images that are not anatomically correct or consistent with the expected number of fingers or the way hands move.

  • What are some potential solutions to improve AI's ability to generate images of hands?

    -Potential solutions include increasing the size and quality of the training datasets, using human feedback to guide the learning process, and possibly retraining the models with a focus on the structural understanding of hands.

  • Why might AI art models perform better in generating certain types of images, like natural scenery?

    -AI art models might perform better in generating natural scenery because these subjects often have more available data and less complex structural requirements than something like hands, which have a high degree of variability and require a detailed understanding of their function.



๐Ÿค– AI Art's Struggle with Human Hands

This paragraph discusses the peculiar challenge AI art models face when attempting to depict human hands. Despite being able to generate complex and varied images, from post-apocalyptic giraffes to historical figures in modern attire, AI struggles with the intricacies of hands. The issue isn't just a minor glitch but a significant limitation in AI's pattern recognition capabilities. Unlike humans who learn to recognize and draw hands through years of observing and handling objects, AI relies on analyzing images and descriptions from the web. The AI's learning process is likened to being trapped in a museum, only able to learn from static pictures and placards, lacking the dynamic interaction humans have with the world. This limitation is highlighted by the AI's inability to simplify complex structures like hands into basic forms, unlike human artists who break down hands into simpler shapes before adding details.


๐Ÿ“š Data Scarcity and the Complexity of Hands in AI Art

The second paragraph delves into the reasons behind AI's difficulty with hands in art generation. Three primary factors are identified: data size and quality, the functional complexity of hands, and a low margin for error in their depiction. The AI's training data often lacks the diversity and quantity needed to accurately learn the structure and movement of hands. Unlike the abundance of face-related data, hand datasets are fewer and less detailed, leading to an incomplete understanding of hands by the AI. Additionally, hands are highly versatile, capable of a wide range of movements and poses, which increases the complexity of their representation. This diversity, coupled with the AI's lack of inherent bias or้ซ˜ๆ ‡ๅ‡† (high standards) for accuracy, results in hands that may appear 'hand-like' but not anatomically correct. The paragraph also touches on the potential for improvement, with newer AI models showing some progress, and discusses potential solutions such as increasing the AI's exposure to more images or incorporating human feedback to refine the models.



๐Ÿ’กAI art

AI art refers to artwork that is created or influenced by artificial intelligence. In the context of the video, AI art struggles with accurately depicting hands, which is a central theme. The video discusses how AI, through pattern recognition, can generate creative and complex images but faces challenges with certain subjects like hands due to the complexity and variability of their structure.

๐Ÿ’กPattern recognition

Pattern recognition is a process that involves identifying and classifying patterns in data. In the video, it is mentioned that both humans and AI learn to recognize what objects look like through pattern recognition. However, AI's process is likened to being trapped in a museum, learning only from the images and descriptions it has access to, which limits its understanding of how objects actually function in the real world.

๐Ÿ’กData size and quality

Data size and quality pertain to the volume and accuracy of the information used to train AI models. The video highlights that AI has fewer examples to learn from when it comes to hands, compared to other subjects like faces. This scarcity, coupled with the lack of detailed annotations on how hands function, contributes to the difficulty AI faces in generating accurate hand images.

๐Ÿ’กMargin for error

Margin for error refers to the allowable difference between an expected result and the AI's output. In the context of the video, hands have a low margin for error because small inaccuracies are easily noticeable and can significantly impact the realism of the artwork. This contrasts with other elements like backgrounds or less detailed objects, where approximations are more acceptable.

๐Ÿ’กGenerative art models

Generative art models are AI systems designed to create original artwork. The video discusses the challenges these models face, particularly with rendering hands. It also mentions how experts in the field are working to improve these models by increasing the data they train on and refining their understanding of complex structures like hands.

๐Ÿ’กHuman feedback

Human feedback is a process where people provide input on the quality or accuracy of AI-generated content. The video suggests that incorporating human feedback could help improve AI art models by training them to produce images that align more closely with human preferences and expectations, particularly for complex subjects like hands.


Simplification is the process of breaking down complex subjects into basic forms to understand and depict them more easily. Artists simplify hands into basic shapes like a blocky palm and cylindrical fingers before adding details. The video contrasts this with AI's approach, which does not simplify forms and instead relies on patterns in pixels without understanding the underlying structure.


Diversity in the context of the video refers to the wide range of positions, gestures, and actions that hands can take. Unlike faces, which tend to have a more standardized set of features and positions, hands are highly variable, making it more challenging for AI to learn and replicate them accurately in artwork.


Bias, in the context of AI, refers to the inherent preferences or tendencies built into the model based on the data it was trained on. The video points out that humans have a bias towards expecting hands to look a certain way, while AI lacks this bias and thus may not prioritize the accuracy of hands in its generated images.


Annotation is the process of adding descriptive information to data, which helps AI understand the context and details of the data. The video discusses the lack of detailed annotation in hand datasets, which means AI systems do not receive clear instructions on the structure and function of hands, leading to inaccuracies in depiction.

๐Ÿ’กComputing power

Computing power refers to the ability of a computer system to process and manage data. The video mentions that training AI on a larger dataset of images requires significant computing power, which is a challenge that developers are working to overcome in order to improve the quality and accuracy of AI-generated art.


AI art struggles with creating realistic hands, even when generating simple images like a man holding an apple.

The challenge of drawing hands by AI is not just a glitch but provides insight into how AI art operates.

AI learns from patterns in images and descriptions, similar to humans, but lacks the ability to understand the functionality of what it sees.

Unlike humans, AI does not simplify complex subjects like hands into basic forms before adding details.

AI's approach to generating images is more pixel pattern recognition rather than understanding the structure of objects.

Data scarcity and quality issues contribute to AI's difficulty in accurately rendering hands.

Datasets for training AI in art generation often have more examples of faces than hands, leading to a skewed learning environment.

The complexity and variability of hand movements make it challenging for AI to learn the correct form and function of hands.

AI art models have less bias than humans, which results in a lower margin for error when generating images with hands.

The AI's inability to understand hands is evident in the diversity of errors seen in AI-generated hand images.

AI art generators are improving over time, with newer models like Midjourney version 5 showing progress in rendering hands.

AI art models are focusing on aspects that the audience appreciates, even if those details are not always explicitly noticed.

Solutions to improve AI's hand rendering include training the AI on a larger dataset and incorporating human feedback.

The analogy of ChatGPT's use of human feedback to fine-tune its model is suggested as a potential method for improving AI art.

The challenge with hands is indicative of broader issues AI faces with pattern recognition in areas like teeth and abs.

AI art's limitations in rendering hands could be overcome by training on more diverse and extensive datasets.

The potential for human involvement in the training process, such as ranking generated images, could significantly improve AI art quality.

AI art generators are continuously evolving, and their progress in handling complex subjects like hands reflects broader advancements in the field.