Low-Cost Deep Learning for Automated Keyframe Interpolation in Sprite Animation

Sprite-based animation remains prevalent in video game development, but manual in-betweening—generating intermediate frames between keyframes—is labor-intensive. Existing video frame interpolation methods, optimized for photorealistic content, introduce smoothing artifacts when applied to the sharp edges, limited color palettes, and stylized motion characteristic of hand-drawn sprites. We present a deep learning approach for sprite in-betweening that adapts a U-Net encoder-decoder architecture with domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, and alpha-gated blending to preserve pixel-perfect sprite aesthetics. Following data-centric AI principles, we demonstrate that a few carefully curated sprite animation triplets—selected for diversity, quality, and representative edge cases—enable effective training without requiring massive generic datasets. Experimental evaluation using the FLIP perceptual metric shows that our method achieves a mean error of 0.258 on 42 test triplets, outperforming state-of-the-art video interpolation RIFE (0.313, 17.5% worse), traditional optical flow Lucas-Kanade (0.449, 42.6% worse), and general frame synthesis CyclicGen (0.603, 57.2% worse). This study shows that quality-over-quantity data curation and domain-specific architectural adaptations yield better interpolation quality than general-purpose methods trained on orders of magnitude more data, validating the data-centric AI paradigm for specialized visual domains. This is particularly important for small-to-medium-sized game development studios, where budget and time constraints are critical.