Analysis of errors in generative image and video models
Mai, Hanlin
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https://hdl.handle.net/2142/132611
Description
Title
Analysis of errors in generative image and video models
Author(s)
Mai, Hanlin
Issue Date
2025-12-11
Director of Research (if dissertation) or Advisor (if thesis)
Lazebnik, Svetlana
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Generative AI
Diffusion Models
Abstract
Image and video generative models have become increasingly powerful and produce visuals that are more difficult to distinguish from real ones in recent years. Users can create images and videos to make their imaginations come true easier than ever before. However, on closer examination, these models make interesting mistakes. In this thesis, we first discuss a systematic way of analyzing errors in generated images relating to projective geometry and shadows at a population level. We find that generated images can be reliably distinguished from real images by derived geometric features alone without looking at pixels. Then, we introduce methods that can effectively judge whether a generated video is physically plausible for robotic demonstrations.
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