{"ID":2843699,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11644","arxiv_id":"2511.11644","title":"Slow - Motion Video Synthesis for Basketball Using Frame Interpolation","abstract":"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.","short_abstract":"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 F...","url_abs":"https://arxiv.org/abs/2511.11644","url_pdf":"https://arxiv.org/pdf/2511.11644v1","authors":"[\"Jiantang Huang\"]","published":"2025-11-10T02:58:59Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}