Arbitrary Generative Video Interpolation

• URL: http://arxiv.org/abs/2510.00578v1
• Date: Wed, 01 Oct 2025 06:57:10 GMT
• Title: Arbitrary Generative Video Interpolation
• Authors: Guozhen Zhang, Haiguang Wang, Chunyu Wang, Yuan Zhou, Qinglin Lu, Limin Wang,
• Abstract summary: Video frame (VFI) generates intermediate frames from given start and end frames.<n>Existing VFI methods are constrained to synthesize a fixed number of intermediate frames.<n>We present ArbInterp, a novel generative VFI framework that enables efficient synthesis at any timestamp.
• Score: 27.953958715353608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video frame interpolation (VFI), which generates intermediate frames from given start and end frames, has become a fundamental function in video generation applications. However, existing generative VFI methods are constrained to synthesize a fixed number of intermediate frames, lacking the flexibility to adjust generated frame rates or total sequence duration. In this work, we present ArbInterp, a novel generative VFI framework that enables efficient interpolation at any timestamp and of any length. Specifically, to support interpolation at any timestamp, we propose the Timestamp-aware Rotary Position Embedding (TaRoPE), which modulates positions in temporal RoPE to align generated frames with target normalized timestamps. This design enables fine-grained control over frame timestamps, addressing the inflexibility of fixed-position paradigms in prior work. For any-length interpolation, we decompose long-sequence generation into segment-wise frame synthesis. We further design a novel appearance-motion decoupled conditioning strategy: it leverages prior segment endpoints to enforce appearance consistency and temporal semantics to maintain motion coherence, ensuring seamless spatiotemporal transitions across segments. Experimentally, we build comprehensive benchmarks for multi-scale frame interpolation (2x to 32x) to assess generalizability across arbitrary interpolation factors. Results show that ArbInterp outperforms prior methods across all scenarios with higher fidelity and more seamless spatiotemporal continuity. Project website: https://mcg-nju.github.io/ArbInterp-Web/.

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