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Testuje, w jaki sposób albedo i cieniowanie są rozdzielone, jak IC-Light wymusza spójny transport światła i gdzie ponowne oświetlenie AI nadal kończy się niepowodzeniem.

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Pytanie 1 z 8

What does image relighting aim to change?

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  1. What does image relighting aim to change?
    • The lighting, while keeping the subject's identity and shape
    • The subject's identity
    • The camera lens
    • Format pliku obrazu
  2. What does intrinsic image decomposition separate?
    • Foreground and background
    • Red, green and blue channels
    • Albedo and shading
    • Text and images
  3. What are light stages used for in portrait relighting research?
    • Rendering fonts
    • Kompresja wideo
    • Training language models
    • Recording a person under many controlled lighting conditions
  4. What physical principle does IC-Light's training enforce?
    • Conservation of momentum
    • An object's appearance under mixed lights equals the mix of its appearances under each light
    • Light travels faster in glass
    • Shadows are always black
  5. Why is a color filter not true relighting?
    • It cannot move shadows or add highlights on the side facing a new light
    • It changes the subject's identity
    • It requires a GPU
    • Działa tylko na wideo
  6. How is the foreground image given to IC-Light's denoiser?
    • As a text prompt
    • As a CLIP score
    • Through a separate audio encoder
    • As latents concatenated with the noisy latent as extra channels
  7. Which practical step helps avoid glowing edges when relighting a cut-out subject?
    • Raising the step count to 500
    • Converting to grayscale
    • Making a clean matte of the subject first
    • Removing the background prompt
  8. Why does better relighting matter for image authenticity?
    • It makes metadata impossible
    • Inconsistent shadows are a clue to composites, so better relighting makes fakes harder to spot
    • It always adds watermarks
    • It prevents image editing