RenderTTT: Refresh-Supervised Adaptation for Cached Neural Rendering

Layer caching skips repeated deep computation in neural rendering, but transported activations can become stale even when the renderer recomputes current shallow features. We formulate refresh-supervised correction: at each scheduled recomputation, the transported old anchor and the fresh activation provide a supervised pair, and a compact correction state is fit to reduce reuse error. We instantiate three readouts under this framework—RenderTTT-affine (tile-local guided-filter coefficients that remap the transported feature), RenderTTT-mixture (per-tile passive/transport blending), and RenderTTT-spatial (a global channel map from current shallow features to the deep feature correction)—all sharing an exact anchor, a refresh mask, and an age clock. Reconstruction weights and refresh decisions remain fixed. For the spatial readout, the output before clipping is equivalent to a current shallow prediction plus a transported refresh residual; we state the additional error introduced by clipping explicitly. ReFrame is the primary baseline, with architecture-matched transport, fixed-blend and initial-fit-only controls. Across two scenes, RenderTTT-spatial reduces reconstruction MSE by 13.3%–20.2% against the full-network reference (an improvement of 0.620–0.981 dB PSNR), while reducing mean latency and peak allocated memory. RenderTTT-affine and RenderTTT-mixture, along with four complete secondary-placement sequences, retain negative transfer cases. The results identify when current-input correction helps a cached renderer and demonstrate consistent quality and cost advantages.