用特定数据微调模型，实时生成高质量篮球慢动作视频。
Slow - Motion Video Synthesis for Basketball Using Frame Interpolation
- 在篮球数据集上微调RIFE网络，提升运动细节还原能力。
- 慢动作帧质量达34.3dB PSNR与0.949 SSIM，优于现有方法。
- 轻量级界面支持单卡实时生成，适合观众与内容创作者。
篮球转播通常以30-60帧/秒录制，难以捕捉扣篮、变向等快速动作的细节。本文提出一种实时慢动作合成系统，通过在SportsSloMo数据集中提取篮球子集，构建训练三元组，并采用人眼感知导向的随机裁剪策略对RIFE网络进行微调。在保留测试片段上，该模型平均达到34.3 dB的PSNR和0.949的SSIM，较Super SloMo提升2.1 dB，较基线RIFE提升1.3 dB。轻量级Gradio界面在单张RTX 4070 Ti Super显卡上实现约30 fps的4倍慢动作生成。结果表明，针对体育场景的任务特化适配对慢动作生成至关重要，且RIFE在精度与速度间提供了良好平衡，适用于消费级应用。
原文摘要 · Abstract (English)
Basketball broadcast footage is traditionally captured at 30-60 fps, limiting viewers' ability to appreciate rapid plays such as dunks and crossovers. We present a real-time slow-motion synthesis system that produces high-quality basketball-specific interpolated frames by fine-tuning the recent Real-Time Intermediate Flow Estimation (RIFE) network on the SportsSloMo dataset. Our pipeline isolates the basketball subset of SportsSloMo, extracts training triplets, and fine-tunes RIFE with human-aware random cropping. We compare the resulting model against Super SloMo and the baseline RIFE model using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) on held-out clips. The fine-tuned RIFE attains a mean PSNR of 34.3 dB and SSIM of 0.949, outperforming Super SloMo by 2.1 dB and the baseline RIFE by 1.3 dB. A lightweight Gradio interface demonstrates end-to-end 4x slow-motion generation on a single RTX 4070 Ti Super at approximately 30 fps. These results indicate that task-specific adaptation is crucial for sports slow-motion, and that RIFE provides an attractive accuracy-speed trade-off for consumer applications.
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