Why AI art struggles with hands

Vox
4 Apr 202309:56
EducationalLearning
32 Likes 10 Comments

TLDRThe video explores why AI art models struggle to generate realistic hands compared to other body parts. It traces this back to three key factors - the limited hand photo data they train on, the complexity of hand poses and motions, and the low tolerance viewers have for inaccuracies in rendered hands. The narrator contrasts how artists simplify forms to tackle complexity while AIs directly interpolate available data. Though improving, models still fail to capture the precise rules governing hands, unlike faces. More training data and human ranking of results may overcome this, guiding AIs to meet people's higher standards for hands.

Takeaways
  • 😟 AI struggles with drawing realistic hands compared to other body parts
  • 👍 Humans learn to draw hands through pattern recognition from seeing many examples
  • 🤖 AI learns differently - by seeing labeled images without real world interaction
  • 📊 There's less quality hand image data for AI art to train on compared to faces
  • 🖐 Hands can contort in many more positions than predictable parts like faces
  • 😵 The variety of hand poses means the AI has trouble learning clear rules
  • 🎨 Artists simplify subjects like hands into basic shapes before adding detail
  • 👀 AI focuses on textures and lighting details but misses structural rules
  • 🔧 Engineers are working on getting more data and feedback to improve AI hands
  • 😀 Despite issues with hands, AI art keeps advancing in quality and capabilities
Q & A

  • Why does the narrator say that AI art is basically bad at art?

    -The narrator says this because AI models struggle to create anatomically accurate depictions of human hands. This shows that while AI can create art, it lacks fundamental artistic skills like accurately representing real world objects.

  • What are the three main reasons the narrator gives for why AI struggles with drawing hands?

    -The three main reasons are: 1) Lack of quality hand image data 2) The complexity and variability of hand positions/gestures 3) The low margin of error - hands need to look right or they look very wrong

  • How does the way AI learns differ from the way humans learn to draw?

    -Humans simplify complex forms like hands into basic shapes while learning. AI just tries to replicate pixel patterns without understanding anatomy. Humans also get real world experience rotating objects in their hands.

  • What solutions are being worked on to help AI get better at drawing hands?

    -Two main solutions - getting more computing power to train models on vastly more images, and incorporating human feedback to fine tune the models towards what people find convincing.

  • Why does the narrator say AI art models are spending time on things people don't notice?

    -Because the models are decent at generating things like natural scenery that people still appreciate, even if they have flaws people don't notice. The models focus less on tricky things like hands.

  • What is the difference between AI art models and ChatGPT?

    -ChatGPT used human feedback to rate which sentences it generated were good or bad during training. This kind of feedback could allow AI art to better learn what people find convincing.

  • Why can AI generate decent apples but struggle with hands?

    -Apples just need to look apple-like, but hands require very specific anatomical accuracy that the models lack exposure to and understanding of.

  • How are AI art model datasets similar and different to a museum?

    -Similar in that the model sees images and descriptions - but very limited. Different in that humans can touch and rotate real objects for a more complex perspective.

  • Why do hands require a higher standard than things like shirts or apples in AI art?

    -Because inaccurate hands look very obviously wrong and break the illusion, while slight errors in patterns like shirts or apple details are less noticeable.

  • Will AI art models likely improve at depicting hands over time?

    -Yes, newer models like Midjourney v5 have already shown some progress. With more data, computing power, and potentially human feedback, hands should steadily improve.

Outlines
00:00
😮 Why AI struggles to draw hands

Paragraph 1 explains why AI art struggles to draw realistic hands compared to other body parts. It contrasts how humans learn to draw hands through pattern recognition from real-world exposure versus how AI models learn from limited museum-like datasets. The key reasons identified are insufficient hand image data, lack of detail on hand pose/gestures, and low tolerance for errors in depicting hands.

05:03
🤔 Factors behind the AI hand struggle

Paragraph 2 elaborates on 3 main factors making hands difficult for AI art: 1) Smaller datasets of hand images compared to faces, lacking details on hand structure/gestures, 2) Hands are highly complex and versatile unlike predictable faces, and 3) There is little room for error in depicting hands realistically unlike abstract backgrounds.

Mindmap
Keywords
💡pattern recognition
The ability to recognize patterns is how humans and AI learn to identify objects. Humans develop pattern recognition from experiencing the world, while AI develops it from analyzing many images. However, AI's pattern recognition is limited by the images it has seen.
💡simplify
Artists simplify complex forms like hands into basic shapes to make them easier to draw. AI systems don't simplify objects into basic forms so they struggle to draw hands and other complex shapes.
💡annotation
Adding detailed labels and descriptions to images to explain what's shown, like noting facial features. Rich annotation helps AI better understand what it's seeing.
💡bias
Humans are biased to notice when hands in art look wrong since we rely heavily on them. AI lacks this bias so it accepts odd-looking hands as long as they resemble hands.
💡feedback
Getting input from people on which AI-generated images look best can help fine-tune models to improve quality over time.
💡retrain
Updating an AI model's training process with more data or new techniques to enhance its capabilities, like improving ability to generate hands.
💡compute
The computing resources needed to train AI models on huge datasets. Lacking sufficient compute power limits how much data a model can learn from.
💡quality
The accuracy and usefulness of the data used to train AI models impacts their performance. Low-quality or limited training data leads to poor results.
💡engineering
Modifying how AI systems operate to improve performance, like incorporating human feedback. Advances require significant software engineering work.
💡convincing
Making AI-generated images realistic and natural enough that they don't appear obviously fake or computer-made to human viewers.
Highlights

First significant research finding

Introduction of new theoretical model

Analysis reveals important implications

Proposed methods enable new applications

Results show potential for practical impact

Transcripts

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