Neural Controlled Directional Frame Generation for Sprite Animation

Overview & Synopsis

Sprite-based animation remains prevalent in video game development, but production workflows require capabilities beyond traditional in-betweening. While existing neural interpolation methods can generate intermediate frames, animators also need forward extrapolation to predict motion continuations and backward extrapolation for editing and reverse-engineering existing sprite sheets. Current video generation models either operate on natural video content with stochastic diffusion sampling incompatible with production requirements, or focus exclusively on interpolation without addressing extrapolation tasks. We present a deep learning approach for controlled directional frame generation in sprite animation using three specialized expert models that share identical U-Net encoder-decoder architectures and several domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, pixel-shuffle upsampling, and alpha-gated blending. Each model is trained on different temporal orderings of the same curated dataset: previous (frames 2,3>1), middle (frames 1,3>2), and next (frames 1,2>3). We demonstrate that only 280 carefully curated sprite triplets enable effective training for all three directional tasks without requiring massive generic datasets. Evaluation on 42 test triplets using the FLIP metric shows that all three models achieve comparable quality--previous (0.26), middle (0.26), and next (0.27)--with no significant degradation for extrapolation versus interpolation, outperforming general-purpose baselines by 17-57%. With deterministic single-pass generation and compact model, our method provides the first practical solution for controllable any-position frame generation specifically tailored for pixel art sprite animation workflows, validating that quality-over-quantity data curation enables specialized deep learning for niche visual domains.