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NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
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Proposes NaRCan, a canonical-image video editing framework that combines a homography plus residual-MLP deformation field with a LoRA-fine-tuned latent diffusion prior and a hierarchical noise/update schedule to produce natural canonical images.
Problem
Canonical-image video representations optimized only for reconstruction often yield distorted, unnatural canonical images, which break downstream editing tools such as ControlNet and prompt-based or handwritten edits.
Approach
Combine a trainable homography (global motion) with residual deformation MLPs (local motion), and add a diffusion loss from a LoRA-fine-tuned latent diffusion model on the canonical image, applied on a schedule (denser and noisier early, sparser later) that cuts training time from 4.8 hours to 20 minutes.
Key results
- On BalanceCC (50 videos), NaRCan reports short-term warping error 0.0065, long-term 0.0484, and interpolation error 8.365, better than CoDeF, MeDM and Hashing-nvd on all three metrics.
- The hierarchical update schedule cuts training time from 4.8 hours to about 20 minutes (14x) on a single RTX4090 for 100 frames.
- A user study with 36 participants reports highest preference for NaRCan on temporal consistency, text alignment, and overall quality.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NaRCan instead of Hashing-nvd | Video representation · BalanceCC | Short-term E_warp: 0.0070 -> 0.0065 | Helped |
| Use NaRCan instead of Hashing-nvd | Video representation · BalanceCC | Long-term E_warp: 0.0495 -> 0.0484 | Helped |
| Use NaRCan instead of CoDeF | Video representation · BalanceCC | Interpolation error: 8.721 -> 8.365 | Helped |
| Add homography to the residual deformation MLP (ablation) | NaRCan deformation modeling · Video canonical-image representation | Canonical-image faithfulness | Helped |
| Add residual deformation MLP to homography (ablation) | NaRCan deformation modeling · Video canonical-image representation | Local non-rigid transformation modeling | Helped |
| Add diffusion prior to canonical-image training (ablation) | NaRCan · Video canonical-image representation | Canonical-image naturalness | Helped |
| Increase the number of canonical-image separations (ablation) | Separated NaRCan · Video frame reconstruction | Reconstruction quality | Helped |
| Increase the number of canonical-image separations (ablation) | Separated NaRCan · Video | Temporal consistency | Hurt |
| Use hierarchical noise and diffusion-prior update scheduling (ablation) | NaRCan training · Training | Training time: 4.8 hours to 20 minutes; 14 times | Helped |
Surprising findings
- Homography-only or MLP-only deformation fields each fail; the combination is needed for natural canonical images.
- Separating into more canonical images improves reconstruction but reduces temporal consistency, and per-frame separation damages edits due to flow errors.
Limitations
- LoRA fine-tuning per scene is time-consuming (about 30 minutes).
- Diffusion loss sometimes fails to guide generation of natural images under extreme scene changes.
- Separated NaRCan is limited in the number of segments because flow-based warping accumulates errors.
Experimental setup
Datasets: BalanceCC DAVIS
Benchmarks: BalanceCC DAVIS
Metrics: short-term warping error long-term warping error interpolation error user study preference
Baselines: CoDeF Hashing-nvd MeDM CCEdit
Builds on: CoDeF TVFlow Dreamfusion score distillation sampling ControlNet RAVE grid trick
Closest papers by meaning
- DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing (CVPR 2024 · similarity 0.86)
- High-Fidelity Diffusion-Based Image Editing (AAAI 2024 · similarity 0.86)
- FADE: Frequency-Aware Diffusion Model Factorization for Video Editing (CVPR 2025 · similarity 0.86)
- MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers (CVPR 2024 · similarity 0.85)
- CCEdit: Creative and Controllable Video Editing via Diffusion Models (CVPR 2024 · similarity 0.85)
- MotionEditor: Editing Video Motion via Content-Aware Diffusion (CVPR 2024 · similarity 0.85)
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