As an avid Doctor Who fan, I was always fascinated by the story of the show, and this is Marconi Mk 3 - a TV camera which filmed a first episode of this legendary sci fi show back in 1963.
This asset was a perfect opportunity for me to try Plasticity 3D and it done extremely well, just looks at those perfect bakes!
Yes, it is AWESOME. It’s tailored for artists and has exactly what we need to bridge the gap in HS modeling workflow. Imagine using booleans, bevels and all of that stuff caring only about the shape and not about topology at all - that’s what it feels to use Plasticity.
I’m about to write an article about this camera and the role of Plasticity in production, so I’ll definitely tag you when it’s ready!
This is great work! Very realistic and detailed, down to dirt and wear on the paint and metal.
Could you explain your workflow? You mentioned the baking. How did you do the texturing? Did you use something like Substance Painter? Did you have some nice reference photos? or did you have access to the actual camera? At what point did you move to Blender?
I often find myself modeling historical artifacts like this from very poor reference photos. What do you do in a situation like that? Extrapolate based on what you can see?
Otherwise it’s just a bunch of sometimes very lowrez pictures which I’m trying to piece together within my understanding of the asset.
Usually I’m looking for images that will tell me about the overall shape and for the specific materials I’m looking for different object which do have high resolultion photos of it. For example I can see the overall shape of the lens in those small images, but looking for the pictures of any lens to grasp the material itself.
Only the high poly version done in Plasticity, the rest of the model - like low poly, unwrapping, rigging and rendering are all done in Blender.
Yup, gathering as much references of the similar objects do really help with this kind of stuff. I’m also using this neural net for upscaling. sometimes it helps to see more details: https://github.com/xinntao/Real-ESRGAN/releases