4 ms·

Nah I disagree, this feels like a glorification of the process not the end result. Just because having the 3D model in the scene with all the lighting makes the

Nah I disagree, this feels like a glorification of the process not the end result. Just because having the 3D model in the scene with all the lighting makes the end result feel more solid to you because you feel you can see the work that's going into it. In the end diffusion technology can make a more realistic image faster than a rendering engine can. I feel pretty strongly that this pipeline will be the foundation for most of the next decade of graphics and making things by hand in 3D will become extremely niche because lets face it anyone who has worked in 3D it's tedious, it's time consuming, takes large teams and it's not even well paid. The future is just tools that give us better controls and every frame will be coming from latent space not simulated photons. I say this as someone who had done 3D professionally in the past.

pegasus 3y ago
Nah, I agree with GP. Who didn't suggest making 3D scenes by hand, but the opposite: create those 3D scenes using the generative method, use ray-tracing or the like to render the image. Maybe have another pass through a model to apply any touch-ups to make it more gritty and less artificial. This way things can stay consistent and sane, avoiding all those flaws which are so easy to spot today.
- whywhywhywhy 3y ago
  I know exactly what OP suggested but why are you both glorifying the fact there is a 3D scene graph made in the middle and then slower rendering at the end when the tech can just go from the first thing to a better finished thing?
  - pegasus 3y ago
    Because it just can't. And it won't. It can't even reliably produce consistent shadows in a still image, so when we talk video with a moving camera, all bets are off. To create flawless movie simulations through a dynamic and rich 3D world, requires an ability of internally represent that scene with a level of accuracy which is beyond what we can hope generative models to achieve, even with the gargantuan amount of GPU-power behind ChatGPT, for example. ChatGPT, may I remind you, can't even properly simulate large-ish multiplications. I think you may need to slightly recalibrate your expectations for generative tech here.
bbor 3y ago
I find that very unlikely. LLMs seem capable of simulating human intuition, but not great at simulating real complex physics. Human intuition of how a scene “should” look isn’t always the effect you want to create, and is rarely accurate im guessing
- dragonwriter 3y ago
  > LLMs seem capable of simulating human intuition, but not great at simulating real complex physics. Diffusion models aren't LLMs (they may use something similar as their text encoder layer) and they simulate their training corpus, which usually isn't selected solely for physical fidelity, because that's not actually the single criteria for visual imagery outside of what is created by diffusion models.
  - bbor 3y ago
    Huh fair enough. I mean they are large models based on language but I see your point. Even though everything you said is true, I still believe there’s a place for human-constructed logically-explicit simulations and functions. In general, and in visual arts.

pegasus 3y ago

Nah, I agree with GP. Who didn't suggest making 3D scenes by hand, but the opposite: create those 3D scenes using the generative method, use ray-tracing or the like to render the image. Maybe have another pass through a model to apply any touch-ups to make it more gritty and less artificial. This way things can stay consistent and sane, avoiding all those flaws which are so easy to spot today.

whywhywhywhy 3y ago
I know exactly what OP suggested but why are you both glorifying the fact there is a 3D scene graph made in the middle and then slower rendering at the end when the tech can just go from the first thing to a better finished thing?
- pegasus 3y ago
  Because it just can't. And it won't. It can't even reliably produce consistent shadows in a still image, so when we talk video with a moving camera, all bets are off. To create flawless movie simulations through a dynamic and rich 3D world, requires an ability of internally represent that scene with a level of accuracy which is beyond what we can hope generative models to achieve, even with the gargantuan amount of GPU-power behind ChatGPT, for example. ChatGPT, may I remind you, can't even properly simulate large-ish multiplications. I think you may need to slightly recalibrate your expectations for generative tech here.

bbor 3y ago

I find that very unlikely. LLMs seem capable of simulating human intuition, but not great at simulating real complex physics. Human intuition of how a scene “should” look isn’t always the effect you want to create, and is rarely accurate im guessing

dragonwriter 3y ago
> LLMs seem capable of simulating human intuition, but not great at simulating real complex physics. Diffusion models aren't LLMs (they may use something similar as their text encoder layer) and they simulate their training corpus, which usually isn't selected solely for physical fidelity, because that's not actually the single criteria for visual imagery outside of what is created by diffusion models.
- bbor 3y ago
  Huh fair enough. I mean they are large models based on language but I see your point. Even though everything you said is true, I still believe there’s a place for human-constructed logically-explicit simulations and functions. In general, and in visual arts.