Align-FG: Efficient Game Frame Generation with Target-Aligned Motion and Progressive Refinement

Game frame generation improves temporal smoothness by synthesizing intermediate frames with limited additional rendering cost. Existing two-stage methods generate complementary warped candidates using image-based optical flow and engine-provided motion vectors (MVs), and subsequently fuse them for frame synthesis. However, intermediate motion derived from engine MVs under a linear motion assumption can misalign the estimated initial positions with their targets, while standalone optical-flow estimation introduces substantial computational overhead. We present Align-FG, an efficient framework that addresses both limitations with two complementary designs. First, Target-Aligned Motion Generation (TAMG) uses engine MVs and depth during training to construct a target-aligned bidirectional geometric prior, reducing initialization errors and allowing the network to focus on appearance changes that are poorly described by engine MVs, such as reflections, shadows, and semi-transparent effects. At inference, the same motion representation is constructed from endpoint MVs under a linear motion assumption, enabling deployment without access to the target frame. Second, we develop a lightweight one-stage Progressive Motion Fusion and Refinement (PMFR) network that adaptively fuses propagated flow with the geometric motion prior and predicts residual corrections across scales, incorporating appearance cues without a standalone optical-flow estimator. Experiments on our Unreal Engine-based and real-game datasets show that Align-FG improves perceptual quality over the evaluated baselines, while generating a $1280\times 720$ frame in $1.60$ ms on an NVIDIA L20 GPU, approximately $6$–$7\times$ faster than RIFE and IFRNet.