MiVID: Multi-Strategic Self-Supervision for Video Frame Interpolation using Diffusion Model

Noname manuscript No. (will be inserted by the editor) Abstract Video Frame Interpolation (VFI) remains a cornerstone in video enhancement, enabling temporal upscaling for tasks like slow-motion rendering, frame rate conversion, and video restoration. While classical methods rely on optical flow and learning-based models assume access to dense ground-truth, both struggle with occlusions, domain shifts, and ambiguous motion. This article introduces MiVID, a lightweight, self-supervised, diffusion-based framework for video interpolation. Our model eliminates the need for explicit motion estimation by combining a 3D U-Net backbone with transformer-style temporal attention, trained under a hybrid masking regime that simulates occlusions and motion uncertainty. The use of cosine-based progressive masking and adaptive loss scheduling allows our network to learn robust spatiotemporal representations without any high-frame-rate supervision.Our frame-Priyansh Srivastava School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India E-mail: priyansh0305@gmail.com Romit Chatterjee School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India E-mail: chatterjeeromit86@gmail.com Abir Sen (Corresponding Author) School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India E-mail: abir.senfcs@kiit.ac.in MiVID is trained entirely on CPU using the datasets and 9-frame video segments, making it a low-resource yet highly effective pipeline.