BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions
원문 초록
Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames. In this paper, we propose (i) a novel motion description map, Bidirectional Motion field (BiM), to effectively describe non-uniform motions; (ii) a BiM-guided Flow Net (BiMFN) with Content-Aware Upsampling Network (CAUN) for precise optical flow estimation; and (iii) Knowledge Distillation for VFI-centric Flow supervision (KDVCF) to supervise the motion estimation of VFI model with VFI-centric teacher flows. The proposed VFI is called a Bidirectional Motion field-guided VFI (BiM-VFI) model. Extensive experiments show that our BiM-VFI model significantly surpasses the recent state-of-the-art VFI methods by 26% and 45% improvements in LPIPS and STLPIPS respectively, yielding interpolated frames with much fewer blurs at arbitrary time instances.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion
Multimodal Fusion SLAM with Fourier Attention
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training
Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow
Event-Enhanced Blurry Video Super-Resolution
토론
이 논문과 연결된 포럼 글이 아직 없습니다.