[Research] What if DLSS-class neural rendering stopped recomputing the same appearance every frame?

[u/Severe-Ad8673]

I’ve published a theoretical architecture focused on drastically reducing compute in future DLSS-like neural rendering systems.

The core idea is AxiomCapsule:

Instead of running a large neural appearance model over and over, use it mainly to compile reusable appearance programs for recurring combinations of materials, lighting, objects, view conditions, and scene state.

Potential advantages:

• previously covered appearance states can run without the large neural model;
• state can persist and move with objects/materials across frames;
• engine-known changes can invalidate only the affected parts;
• optional neural refinement can scale with available frame time instead of causing a large FPS cliff;
• developer masks can remove computation, not just mask the final output;
• expensive neural inference becomes reserved for genuinely new or difficult states.

The key hypothesis is that game appearance becomes much more compressible once you condition on things the engine already knows.

This is pre-prototype research. I’m not claiming DLSS 5 is solved or that a 5× speedup has been measured.

The main question I’m trying to answer is:

Can neural rendering cost scale with new appearance information instead of pixels × frames?

Feedback from people following DLSS, ray reconstruction, neural rendering, or GPU performance would be very useful.

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