SG-RIFE: Semantic-Guided Real-Time Intermediate Flow Estimation with Diffusion-Competitive Perceptual Quality
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arXiv
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| Format: | Preprint |
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2025
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| author | Wong, Pan Ben Wu, Chengli Lu, Hanyue |
| author_facet | Wong, Pan Ben Wu, Chengli Lu, Hanyue |
| contents | Real-time Video Frame Interpolation (VFI) has long been dominated by flow-based methods like RIFE, which offer high throughput but often fail in complicated scenarios involving large motion and occlusion. Conversely, recent diffusion-based approaches (e.g., Consec. BB) achieve state-of-the-art perceptual quality but suffer from prohibitive latency, rendering them impractical for real-time applications. To bridge this gap, we propose Semantic-Guided RIFE (SG-RIFE). Instead of training from scratch, we introduce a parameter-efficient fine-tuning strategy that augments a pre-trained RIFE backbone with semantic priors from a frozen DINOv3 Vision Transformer. We propose a Split-Fidelity Aware Projection Module (Split-FAPM) to compress and refine high-dimensional features, and a Deformable Semantic Fusion (DSF) module to align these semantic priors with pixel-level motion fields. Experiments on SNU-FILM demonstrate that semantic injection provides a decisive boost in perceptual fidelity. SG-RIFE outperforms diffusion-based LDMVFI in FID/LPIPS and achieves quality comparable to Consec. BB on complex benchmarks while running significantly faster, proving that semantic consistency enables flow-based methods to achieve diffusion-competitive perceptual quality in near real-time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_18241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SG-RIFE: Semantic-Guided Real-Time Intermediate Flow Estimation with Diffusion-Competitive Perceptual Quality Wong, Pan Ben Wu, Chengli Lu, Hanyue Computer Vision and Pattern Recognition Real-time Video Frame Interpolation (VFI) has long been dominated by flow-based methods like RIFE, which offer high throughput but often fail in complicated scenarios involving large motion and occlusion. Conversely, recent diffusion-based approaches (e.g., Consec. BB) achieve state-of-the-art perceptual quality but suffer from prohibitive latency, rendering them impractical for real-time applications. To bridge this gap, we propose Semantic-Guided RIFE (SG-RIFE). Instead of training from scratch, we introduce a parameter-efficient fine-tuning strategy that augments a pre-trained RIFE backbone with semantic priors from a frozen DINOv3 Vision Transformer. We propose a Split-Fidelity Aware Projection Module (Split-FAPM) to compress and refine high-dimensional features, and a Deformable Semantic Fusion (DSF) module to align these semantic priors with pixel-level motion fields. Experiments on SNU-FILM demonstrate that semantic injection provides a decisive boost in perceptual fidelity. SG-RIFE outperforms diffusion-based LDMVFI in FID/LPIPS and achieves quality comparable to Consec. BB on complex benchmarks while running significantly faster, proving that semantic consistency enables flow-based methods to achieve diffusion-competitive perceptual quality in near real-time. |
| title | SG-RIFE: Semantic-Guided Real-Time Intermediate Flow Estimation with Diffusion-Competitive Perceptual Quality |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.18241 |