Copyright Notice
Building Vision AI: From Pixels to Generative Models. Copyright © 2026 Alexander Apartsin & Yehudit Aperstein. All rights reserved.
No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder, except for brief quotations embodied in reviews, scholarly commentary, and other noncommercial uses permitted by applicable copyright law.
Edition
This is the Second Edition, published in 2026. The edition line appears in the footer of every page of the book; if the footer on the page you are reading names a different edition, you are reading a later revision of this work.
The Second Edition deepens Part IV (Generative Vision Models) and the generative-modeling foundations into full, self-contained graduate-level derivations so the book can serve as the sole text for an advanced course on generative modeling and world models. New and substantially expanded material includes: the two derivations of the variational lower bound and its rate-distortion view; vector-quantized latents with the straight-through estimator; the Hyvarinen score-matching identity, denoising score matching, and noise-conditional score networks; the variance-exploding and variance-preserving stochastic differential equations, the reverse-time SDE, the probability-flow ODE, predictor-corrector sampling, and the EDM design space; classifier and classifier-free guidance and diffusion for inverse problems; flow matching, rectified flow, and consistency models; latent-dynamics world models (recurrent state-space models, the Dreamer line, and decoder-free latent control); generative world simulators; joint-embedding predictive architectures; and a dedicated world-model evaluation toolkit. A unified-family synthesis and a graduate course map (Appendix C) tie the material to a thirteen-week syllabus.
Use of Code Examples
The code examples in this book exist to be used. You may run, modify, and incorporate them into your own programs and projects, commercial or otherwise, without seeking permission, and attribution is appreciated but not required for ordinary use. Reproducing a significant portion of the book's code or prose in another publication, in teaching materials offered for sale, or in a product whose value derives substantially from this book's content does require written permission from the copyright holder.
Trademarks
Product and library names mentioned in this book, including Python, NumPy, OpenCV, scikit-image, Pillow, PyTorch, Hugging Face, ONNX, TensorRT, OpenVINO, COLMAP, ComfyUI, and others, are trademarks or registered trademarks of their respective owners. They are used in this book for identification and explanation only, without intent of infringement, and their appearance does not imply any affiliation with or endorsement by the trademark holders.
Disclaimer of Warranty
This book is provided on an "as is" basis, without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, or non-infringement. While the authors and the production pipeline have taken care to ensure accuracy, the field this book describes moves quickly: libraries change their interfaces, models are superseded, benchmark numbers shift, and links age. Neither the authors nor any distributor of this book shall be liable for any loss or damage arising from the use of the information or code contained herein. Verify behavior in your own environment before relying on it in production systems.
Production Disclosure
This book was produced by Alexander Apartsin and Yehudit Aperstein together with a 42-agent AI writing pipeline operating under their direction, as described in About the Authors. Editorial responsibility for the content rests with the human authors.
Third-Party Links and Datasets
The book links to external papers, repositories, documentation, and datasets. These resources are the property of their respective owners and are governed by their own licenses, which you should review before use; dataset licensing notes in Appendix B are summaries, not legal advice. External URLs were verified during production but may move or change after publication.
Permissions
For permission requests beyond the scope described above, including translation, redistribution, excerpting, and adaptation, contact the copyright holders, Alexander Apartsin and Yehudit Aperstein.