3 ms·

Funny how it is not good at heels. Who knew those would be hard?

by ncmncm 5y ago

nighthawk454 5y ago
that kind of high-frequency detail is easy to lose in a model and/or from smoothing techniques
- bj-rn 5y ago
  Does this also apply to hands? They look mangled in these examples.
  - doodlebugging 5y ago
    We're probably lucky that they chose to model fully clothed subjects. Based on the smearing we see in some translated details like the fingers and examples of holes in skirts we could have some comically bad output.
Fiahil 5y ago
Same as many other instances of specialized ML models, their results look good generally, but completely fall apart under scrutiny. Remember GPT-3 blogpost writing? Although, they are getting better years after years, so maybe we'll be out of the uncanny valley at some point ?
- pingeroo 5y ago
  What was the issue with GPT-3 & blog posts?
  - skybrian 5y ago
    The generated text often seems okay if you're skimming, but if you think about what they're saying, it's rarely meaningful.
    - bawolff 5y ago
      I don't think that really came as a surprise to anyone... nobody thought that gpt-3 was going to make sensible long-form narratives.
      - elb2020 5y ago
        Yet, people do seem to think that DALL-E 2 is going to be able to make sensible "long-form" illustrations and paintings.
- plafl 5y ago
  I have been recently looking at several "3d scanning" from monocular images/videos solutions and although they make for impressive demos I'm afraid they are a few years away from robust results. On one hand you have traditional photogrammetry based on classical image descriptors (from the beginning of the 2000s). A nice open source solution is Meshroom [1]. I have very little experience as I have just tinkered a little but I would say they work OK with geometry of medium complexity and detailed textures. They fail horribly with untextured objects, like for example a candle. They are semi-automatic: you upload some photos and run the pipepiline but for best results it's easy to tweak as there are a lot of knobs to try. On the other hand you have these deep learning research papers. A lot of them have Colab notebooks you can try yourself (which is awesome, thank you). They can give you these very nice demos for some kind of objects (the ones we have a lot of training data, like human bodies), but they are not truly general and sometimes will generate artifacts. There is some opportunity here if they are integrated in a semi-supervised workflow, like this [2]. Anyway, just curious and not an expert. [1] https://alicevision.org/ https://alicevision.org/ [2] https://keentools.io/products/facebuilder-for-blender https://keentools.io/products/facebuilder-for-blender
deleted 5y ago
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Funny how it is not good at heels. Who knew those would be hard?

by ncmncm 5y ago

Funny how it is not good at heels. Who knew those would be hard?

nighthawk454 5y ago

that kind of high-frequency detail is easy to lose in a model and/or from smoothing techniques

bj-rn 5y ago
Does this also apply to hands? They look mangled in these examples.
- doodlebugging 5y ago
  We're probably lucky that they chose to model fully clothed subjects. Based on the smearing we see in some translated details like the fingers and examples of holes in skirts we could have some comically bad output.

Fiahil 5y ago

Same as many other instances of specialized ML models, their results look good generally, but completely fall apart under scrutiny. Remember GPT-3 blogpost writing? Although, they are getting better years after years, so maybe we'll be out of the uncanny valley at some point ?

pingeroo 5y ago
What was the issue with GPT-3 & blog posts?
- skybrian 5y ago
  The generated text often seems okay if you're skimming, but if you think about what they're saying, it's rarely meaningful.
  - bawolff 5y ago
    I don't think that really came as a surprise to anyone... nobody thought that gpt-3 was going to make sensible long-form narratives.
    - elb2020 5y ago
      Yet, people do seem to think that DALL-E 2 is going to be able to make sensible "long-form" illustrations and paintings.
plafl 5y ago
I have been recently looking at several "3d scanning" from monocular images/videos solutions and although they make for impressive demos I'm afraid they are a few years away from robust results. On one hand you have traditional photogrammetry based on classical image descriptors (from the beginning of the 2000s). A nice open source solution is Meshroom [1]. I have very little experience as I have just tinkered a little but I would say they work OK with geometry of medium complexity and detailed textures. They fail horribly with untextured objects, like for example a candle. They are semi-automatic: you upload some photos and run the pipepiline but for best results it's easy to tweak as there are a lot of knobs to try. On the other hand you have these deep learning research papers. A lot of them have Colab notebooks you can try yourself (which is awesome, thank you). They can give you these very nice demos for some kind of objects (the ones we have a lot of training data, like human bodies), but they are not truly general and sometimes will generate artifacts. There is some opportunity here if they are integrated in a semi-supervised workflow, like this [2]. Anyway, just curious and not an expert. [1] https://alicevision.org/ https://alicevision.org/ [2] https://keentools.io/products/facebuilder-for-blender https://keentools.io/products/facebuilder-for-blender

deleted 5y ago

[deleted]