Supplementary Data for: AI Frame Generation on Budget Hardware: An Empirical Analysis of Performance, and Latency

This dataset contains the supplementary benchmarking data, custom Python scripts, and video comparisons for the research paper "AI Frame Generation on Budget Hardware: An Empirical Analysis of Performance, and Latency." It provides a comprehensive evaluation of Real-Time Intermediate Flow Estimation (RIFE) models applied to budget-tier computing environments, specifically comparing dedicated (NVIDIA RTX 3050A) and integrated (Intel UHD Graphics) GPU performance. The uploaded archive includes: Custom Software: A Python-based Blender addon developed to interface the RIFE AI model directly with the rendering pipeline. Raw Telemetry: Performance statistics tracking CPU/GPU utilization, framerates (FPS), system latency, and thermal metrics across Live and VFX rendering workloads. Visual Benchmarks: High-resolution 2x (60 FPS) and extreme 6x (180 FPS) scalar video interpolations generated from a 30 FPS baseline for visual artifact and latency analysis. Project Files: Source files and isolated hardware testing environments used to generate the control footage.