SwiftI2V: Efficient High-Resolution Image-to-Video Generation via Conditional Segment-wise Generation
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| Authors | YaoYang Liu et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2605.06356 |
| Download | |
| Code | https://github.com/HKUST-LongGroup/SwiftI2V |
Abstract
High-resolution image-to-video (I2V) generation aims to synthesize realistic temporal dynamics while preserving fine-grained appearance details of the input image. At 2K resolution, it becomes extremely challenging, and existing solutions suffer from various weaknesses: 1) end-to-end models are often prohibitively expensive in memory and latency; 2) cascading low-resolution generation with a generic video super-resolution tends to hallucinate details and drift from input-specific local structures, since the super-resolution stage is not explicitly conditioned on the input image. To this end, we propose SwiftI2V, an efficient framework tailored for high-resolution I2V. Following the widely used two-stage design, it addresses the efficiency--fidelity dilemma by first generating a low-resolution motion reference to reduce token costs and ease the modeling burden, then performing a strongly image-conditioned 2K synthesis guided by the motion to recover input-faithful details with controlled overhead. Specifically, to make generation more scalable, SwiftI2V introduces Conditional Segment-wise Generation (CSG) to synthesize videos segment-by-segment with a bounded per-step token budget, and adopts bidirectional contextual interaction within each segment to improve cross-segment coherence and input fidelity. On VBench-I2V at 2K resolution, SwiftI2V achieves performance comparable to end-to-end baselines while reducing total GPU-time by 202x. Particularly, it enables practical 2K I2V generation on a single datacenter GPU (e.g., H800) or consumer GPU (e.g., RTX 4090).
Engineering Breakdown
Plain English
SwiftI2V generates high-resolution videos (2K) from a single image while maintaining the input's visual details and realistic motion. The paper tackles the core inefficiency problem: existing end-to-end approaches are too expensive (memory/latency), while cascaded approaches (low-res generation + super-resolution) hallucinate details because the super-resolution stage doesn't know what the original image looked like. SwiftI2V uses a two-stage design that conditions the super-resolution step directly on the input image, solving both efficiency and quality problems.
Key Engineering Insight
The critical insight is that cascaded video generation fails not because of the pipeline itself, but because decoupling stages loses input conditioning. By explicitly feeding the original image into the final super-resolution stage, you get cheap low-resolution generation plus high-fidelity detail recovery without expensive end-to-end processing.
Why It Matters for Engineers
Production video generation at 2K resolution hits hard memory/latency constraints on real hardware. Most teams either compromise on resolution or build expensive multi-GPU setups. This work directly addresses the engineering tradeoff between throughput and output quality—you can likely serve more users with better quality output on the same infrastructure.
Research Context
I2V generation has moved from low-res toy demos to practical 1080p/2K targets, but scaling hits the efficiency wall. Prior two-stage approaches failed because super-resolution treated video as generic content, not image-conditioned synthesis. This paper reframes the problem: make super-resolution explicitly aware of what details it should preserve from the input, enabling both fast generation and faithful output.
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