Answers.Body: <p>In <strong>Procedural Content Generation (PCG)</strong> in computer games, you can generate procedurally a variety of contents, from <em>characters</em>, <em>weapons</em>, <em>songs</em>, <em>maps</em>, <em>dungeons</em>, <em>story</em>, and even entire <em>NPCs</em>. In few words, you start from a set of base images and rules, you then apply these rules on a random subset of images (note that the rules are usually based on some random generator, so there is variability and perturbations in them too - but this depends mostly on the role of PCG and what type of content you want to design.) to create new images. You can repeat this process at each gameplay, for example, to generate an entire new experience for the player or use the method to generate candidates, which are then refined by hand - so picking the very best generated ones.</p>
  <p>In contrast, <strong>generative AI</strong> (or deep generative models) starts by training a (usually large) model on a (large) dataset, such that when noise is injected (or, as in some other approach, the noise is the input) you can generate (i.e., synthesize) a new sample that looks like a plausible image from that dataset. In this regard, there are multiple effective methods like <em>generative adversarial networks</em> and <em>diffusion models</em>, but in some cases also <em>variational autoencoders</em> and <em>normalizing flows</em> are fine.</p>
  <p>The difference between PCG and GenAI is that, in the former case you design by hand the base images and rules, whereas in the second case you design the model and, in some cases, also curate the dataset. So, in PCG the generation is controlled by your rules, whereas in GenAI is determined by what the model has successfully learned.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-12T13:49:40.123Z
Answers.LastActivityDate: 2023-10-12T13:49:40.123Z
Answers.UserDisplayName: Luca Anzalone
Body: <p>I am new here, and apologize if this question is off-topic.</p>
  <hr />
  <p>I know that AI generated images are based on a set or database of real images created by real artists.</p>
  <p>In game development, I have heard of the term &quot;<strong>procedurally generated</strong>&quot; images.</p>
  <p>My questions are :</p>
  <ol>
  <li>Are &quot;<strong>procedurally generated</strong>&quot; images generated from a set of real
  images created by real artists (as <strong>AI generated</strong> images are) ?</li>
  <li>What are the main differences between &quot;<strong>procedurally generated</strong>&quot;
  images and <strong>AI generated</strong> images ?</li>
  </ol>
  
Tags: <machine-learning><ai-design><game-ai>
Title: Do "procedurally generated" images use a set of base images to generate new images (as AI generated images do)?
UserDisplayName: Job_September_2020
ThreadText: # Do "procedurally generated" images use a set of base images to generate new images (as AI generated images do)?
  
  (Asked by: username_0 on 2023-10-10)
  
  I am new here, and apologize if this question is off-topic.
  
  ---
  
  I know that AI generated images are based on a set or database of real images created by real artists.
  
  In game development, I have heard of the term "**procedurally generated**" images.
  
  My questions are :
  
  1. Are "**procedurally generated**" images generated from a set of real
     images created by real artists (as **AI generated** images are) ?
  2. What are the main differences between "**procedurally generated**"
     images and **AI generated** images ?
  
  **Tags:** machine-learning, ai-design, game-ai
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-10-12. Score: 2 | Accepted answer
  
  In **Procedural Content Generation (PCG)** in computer games, you can generate procedurally a variety of contents, from *characters*, *weapons*, *songs*, *maps*, *dungeons*, *story*, and even entire *NPCs*. In few words, you start from a set of base images and rules, you then apply these rules on a random subset of images (note that the rules are usually based on some random generator, so there is variability and perturbations in them too - but this depends mostly on the role of PCG and what type of content you want to design.) to create new images. You can repeat this process at each gameplay, for example, to generate an entire new experience for the player or use the method to generate candidates, which are then refined by hand - so picking the very best generated ones.
  
  In contrast, **generative AI** (or deep generative models) starts by training a (usually large) model on a (large) dataset, such that when noise is injected (or, as in some other approach, the noise is the input) you can generate (i.e., synthesize) a new sample that looks like a plausible image from that dataset. In this regard, there are multiple effective methods like *generative adversarial networks* and *diffusion models*, but in some cases also *variational autoencoders* and *normalizing flows* are fine.
  
  The difference between PCG and GenAI is that, in the former case you design by hand the base images and rules, whereas in the second case you design the model and, in some cases, also curate the dataset. So, in PCG the generation is controlled by your rules, whereas in GenAI is determined by what the model has successfully learned.
  
  
  ---
  
  