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Particles in Nuke 23/4

Particles in nuke can be used to make cool effects to simulations like rain or fire ember, this is very useful in the late post production stages when it involves simple elements which does not require FX department.

  • There are lots of possibility in particles and Nuke provides some presents for start-up and all of them can be modified through attributes like wind, turbulence etc.
  • We can use the card as the particle emitter and we can manipulate the position of the card to change the emission as well, the bigger the card the bigger the emission surface
  • By default the particles outputs Alpha value
  • We can use a texture as a point colour while emitting the particles.
  • The image itself cam be used as a particle and when doing this it’s advisable to use the extremely low res images since using full quality image could use all the GPU RAM.
  • We can also use multiple images as particles and also 3D objects as the particles as well, and we can also animate 3D object which will apply into the instanced particles as well.
  • When using 3D object it is advisable to use low poly objects.
  • There are particle properties nodes like drag, gravity and turbulence, we can use these to create cool effects.
  • The particles by default generates Z depth data, we can use this to create camera defocus in nuke.
  • The particle emitter can also be of a geometry type.

Creating embers for fire with Particle system

  • First we use a grid as an emitter geo and we rotate it in random direction to get variation in the emmission of the particles.
  • Then we use various forces like wind, drag and turbulent noise to make the particles act more organic and on the specification of a real-life fire ember.
  • And finally to get a visual we place a camera and a scanline render node to render the image in the viewport.
  • To make the particle disappear we create a particle curve node to animate the alpha based on the age.
  • To make the particles more believable we use the embedded z_depth pass into the defocus node.
  • Change the colour of the particle to orange with grade.
  • And if there is an movement in the plate we track and apply the transform accordingly.
  • Create a base glow of the ember and create mask of that confining withing the core of the fireplace so that the centre region fire embers looks intense.
  • Add that soft glow which we created into the plate.
  • Create a separate glow of particle and mask the centre again and grade with extra intensity and merge the resultant image into the BG plate.
  • Now to create a fake smoke field we create a noise using the FractalBlur node and tweak the parameters and shuffle out the green and blue channels and use the green channel as the alpha channel aswell.
  • Animate the noise across the screen.
  • Now blur extremely the embers which we created earlier and copy the alpha from the fractalblur to the blured immage and premult to get the result.
  • And finally merge it to the plate.
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Nuke

Copycat in Nuke 16/04

Copycat node is a machine learning algorithm introduced in 2020 in nuke, it is used to train with a set of images and generate in-between frames with data which matches the given intervals of frame. the data keeps getting better when we feed more data and the output becomes more accurate.

Examle:

We can use the copycat to clean-up the plate by training the node with intermediate cleaned up frames and letting the machine learning to create the in-between frames.

  • Create the Copycat node and set the directory
  • Copycat node should work on linear colorspace input.
  • Adjust epochs/steps of calculations as per the shot requirement / Think about epochs like sampling in 3D/ MB.
  • More the epochs more the time to train and also the PC might be unusable during the calculations.
  • In the current version of copycat we can pause the training process to check the results.
  • In the advance settings having large model size might take a lot time to calculate so need to be carful on using these settings.
  • We can use any previous training data and use it as a checkpoint to aid the machine learning.
  • We can use the same principle to many things like the roto