Answers.Body: <p>In computer vision, the adjectives <em>dense</em> and <em>sparse</em> are used in a variety of tasks (e.g. <a href="https://en.wikipedia.org/wiki/Optical_flow" rel="nofollow noreferrer">optimal flow</a>), but they are commonly used in the context of the <a href="https://www.mia.uni-saarland.de/zimmer/phd-thesis" rel="nofollow noreferrer">correspondence problem</a>, which is the problem of finding a map (or correspondence) between pixels of two images (e.g. two successive frames of a video). In this context, these adjectives thus refer to the number of pixels of the image that are used to solve this specific task. A <em>dense correspondence</em> is thus a correspondence between two images using all (or, at least, <em>many</em>) pixels. In other words, a dense correspondence attempts to map all (or many) pixels of an image to all (or many) pixels of another image.</p>
  
  <p>In the case of <a href="https://arxiv.org/pdf/1802.00434.pdf" rel="nofollow noreferrer">DensePose</a>, the correspondences are between RGB images and surface-based representation of the human body (the figures of the paper illustrates this).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-07-09T09:26:33.587Z
Answers.LastActivityDate: 2019-07-09T09:32:01.153Z
Answers.LastEditDate: 2019-07-09T09:32:01.153Z
Answers.UserDisplayName: nbro
Body: <p>I've recently come across an amazing work for human pose estimation: <a href="https://arxiv.org/pdf/1802.00434.pdf" rel="nofollow noreferrer">DensePose: Dense Human Pose Estimation In The Wild</a> by Facebook. </p>
  
  <p>In this work, they have tackled the task of dense human pose estimation using discriminative trained models.</p>
  
  <p>I do understand that "correspondence" means how well pixels in one image correspond to pixels in the second image (specifically, here - 2D to 3D).</p>
  
  <p>But what does "dense" means in this case?</p>
  
Tags: <machine-learning><computer-vision><terminology><papers>
Title: What is "dense" in DensePose?
UserDisplayName: Alex Goft
ThreadText: # What is "dense" in DensePose?
  
  (Asked by: username_0 on 2019-07-09)
  
  I've recently come across an amazing work for human pose estimation: [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/pdf/1802.00434.pdf) by Facebook.
  
  In this work, they have tackled the task of dense human pose estimation using discriminative trained models.
  
  I do understand that "correspondence" means how well pixels in one image correspond to pixels in the second image (specifically, here - 2D to 3D).
  
  But what does "dense" means in this case?
  
  **Tags:** machine-learning, computer-vision, terminology, papers
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-07-09. Score: 0 | Accepted answer
  
  In computer vision, the adjectives *dense* and *sparse* are used in a variety of tasks (e.g. [optimal flow](https://en.wikipedia.org/wiki/Optical_flow)), but they are commonly used in the context of the [correspondence problem](https://www.mia.uni-saarland.de/zimmer/phd-thesis), which is the problem of finding a map (or correspondence) between pixels of two images (e.g. two successive frames of a video). In this context, these adjectives thus refer to the number of pixels of the image that are used to solve this specific task. A *dense correspondence* is thus a correspondence between two images using all (or, at least, *many*) pixels. In other words, a dense correspondence attempts to map all (or many) pixels of an image to all (or many) pixels of another image.
  
  In the case of [DensePose](https://arxiv.org/pdf/1802.00434.pdf), the correspondences are between RGB images and surface-based representation of the human body (the figures of the paper illustrates this).
  
  
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