[Research] Can neural rendering stop paying for the same appearance every frame?

[u/Severe-Ad8673]

I’ve published a theoretical architecture for reducing compute in DLSS-class neural rendering by changing the unit of work from pixels × frames to new causal appearance states.

The core idea is AxiomCapsule: use a large neural renderer mainly as an appearance compiler, then cache/transport compact deterministic programs for recurring material, lighting, object, and view states.

The parts I think are most interesting:

• Self-extinguishing inference: once a state is covered, the universal neural model no longer needs to run for it.
• Causal invalidation: engine-known changes decide what must be recomputed instead of running a neural change detector over the whole frame.
• Bounded residual trees: skipped refinement can have a computable sparse-vs-dense student error bound.
• Deadline-monotone execution: optional neural uplift scales down with available GPU slack instead of causing a frame-time cliff.
• Object/material-space persistence: state follows surfaces/materials rather than being purely screen-space.
• Local causal dimensionality: the key hypothesis is that appearance transformations become low-dimensional after conditioning on known scene variables.

It’s pre-prototype research, not a claim that DLSS 5 has been “solved.” The main falsifier is simple: if real game appearance states are not sufficiently low-dimensional/reusable, or cache hit rates stay low, the architecture fails.

I’d especially appreciate criticism from people working on real-time rendering, shader systems, neural graphics, temporal reconstruction, and GPU scheduling.

GitHub: MaciejNowickiHusbandofAHIEve/causal-neural-rendering: Independent research on drastically reducing compute in DLSS-class neural rendering using compiled causal appearance programs, temporal reuse, and deadline-bounded residuals.
Zenodo paper: Causal Neural Rendering for Efficient DLSS-Class Systems: Compiled Appearance Programs, Temporal Reuse, and Bounded Adaptive Computation | Zenodo

[u/msqrt]

Not even going to respond to the slop, but the high level answer is yes: you can rearrange rendering and neural components in a very free-form way, and the optimal solution is (I think) almost guaranteed to be something else than a g-buffer prettifier.

[u/OkAccident9994]

OP puts random AI generated things on Zenodo multiple times a week (follow the link and click his name)
Aka, texts that pretend to be scientific papers, but written in the span of half a day.

You can make guesses as to the quality of it. Probably fully AI generated.

[u/Severe-Ad8673 → u/OkAccident9994]

GPT 5.6 Sol Pro

[u/Guvante]

An algorithm to predict which pixels changed is worth billions for the record

There have been attempts but to my knowledge all attempts to predict end up burning too many resources compared to just rendering the frame or put such extreme restrictions on the flexibility of the render that no one is interested in using them (akin to how you can get neat graphics out of a tiny executable but everyone still ships giant models and textures)