Production Efficiency of 2D Pixel Art Through 3D Model Transformation Using Node-Based Compositing - Najwa Nur Azizah Sanusi; Samuel Gandang Gunanto; Mohammad Arifian Rohman (Institut Seni Indonesia Surakarta, 2026)
The game development industry demands high efficiency in visual asset production without compromising aesthetics. While 2D pixel art maintains consistent market appeal, its conventional manual frame-by-frame method is structurally limited in terms of consistency and scalability. This research designs and validates a deterministic hybrid pipeline for 3D-to-2D pixel art character asset production using Blender's Compositing Node, employing a Research and Development (R&D) approach combined with experimental methods. The pipeline integrates Rasterization Algorithms, Grid Snapping, and Downsampling Theory, with production time, visual quality, and reusability were evaluated objectively using screen-recording analysis. Three key findings were obtained. First, the pixel conversion formula Value = Render Resolution ÷ Target Resolution was mathematically validated across five resolution scenarios, yielding perfect matrix partitions without pixel residue; in the critical case (1080 px → 64 px), spatial deviation was only 0.74%, well below the industry tolerance threshold of 5–10%. Second, the pipeline achieved a Break-Even Point (BEP) at n ≈ 0.97, indicating that the reusable component investment (3.21 hours) was recovered before the first character was completed, with efficiency gains growing from 1.66% (1 asset) to 49.73% (10 assets). Third, the identified visual deviation of 1–2 pixels (1.56%–3.13%) falls below the Just Noticeable Difference (JND) threshold of 5–10%, confirming that pixel art aesthetics including outline sharpness, inter-frame proportional consistency, and silhouette readability remain fully intact. These results demonstrate that the proposed pipeline offers a reproducible, scalable, and AI-independent solution for mass production of pixel art character assets.