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TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models
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Proposes TRIP, an image-to-video diffusion model that uses an image noise prior from one-step backward diffusion as a reference noise plus a residual 3D-UNet path, fused by a transformer-based temporal noise fusion module.
Problem
Image-to-video generation with diffusion models struggles to keep subsequent frames both faithfully aligned with the given image and temporally coherent, since independent per-frame noise prediction under-exploits the image-frame relation.
Approach
Reformulates noise prediction as temporal residual learning: a shortcut path derives an image noise prior from the first-frame latent and noised video latents via one-step backward diffusion and uses it as reference noise, while a residual path fine-tunes a 3D-UNet to predict per-frame residual noise; a transformer-based TNF module dynamically fuses both.
Key results
- On WebVid-10M, TRIP reaches F-Consistency4 of 95.36% and FVD 38.9, beating T2V-Zero (91.59, 279) and VideoComposer (88.78, 231).
- On MSR-VTT (zero-shot), TRIP achieves FID 9.68 and FVD 91.3, versus VideoComposer I2V's 31.29 and 208.
- Human evaluation on WebVid-10M shows TRIP preferred over VideoComposer for temporal coherence at 84.4% vs 15.6%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use TRIP instead of T2V-Zero | I2V generation · WebVid-10M | F-Consistency4: 91.59 -> 95.36 | Helped |
| use TRIP instead of VideoComposer | I2V generation · WebVid-10M | F-Consistencyall: 92.52 -> 96.41 | Helped |
| use TRIP instead of VideoComposer | I2V generation · WebVid-10M | FVD: 231 -> 38.9 | Helped |
| use TRIP instead of cINN | I2V generation · DTDB | FID: 31.9 -> 24.8 | Helped |
| use TRIP instead of cINN | I2V generation · DTDB | FVD: 451.6 -> 433.9 | Helped |
| use TRIP instead of ModelScopeT2V | video generation · MSR-VTT | FID: 11.09 -> 9.68 | Helped |
| use TRIP instead of VideoComposer (I2V) | I2V generation · MSR-VTT | FVD: 208 -> 91.3 | Helped |
| concatenate the first-frame image latent along the temporal dimension at the start rather than along the channel dimension (ablation) | TRIP · WebVid-10M | F-Consistency4: 94.77 -> 95.36 | Helped |
| concatenate the first-frame image latent at the start rather than at the end of the video latent sequence (ablation) | TRIP · WebVid-10M | F-Consistency4: 95.17 -> 95.36 | Helped |
| add the shortcut path with image noise prior (ablation) | TRIP · WebVid-10M | F-Consistency4: 94.66 -> 95.36 | Helped |
| replace linear noise fusion with Transformer-based temporal noise fusion (ablation) | TRIP with image noise prior · WebVid-10M | FVD: 43.0 -> 38.9 | Helped |
Limitations
- Evaluation relies on frame-consistency and FVD proxies, since no standard I2V metric exists (stated by authors)
- DTDB and MSR-VTT results are zero-shot, so generalization claims rest on limited benchmarks
Experimental setup
Datasets: WebVid-10M DTDB MSR-VTT
Benchmarks: WebVid-10M DTDB MSR-VTT
Metrics: F-Consistency4 F-Consistencyall FVD FID human preference
Baselines: T2V-Zero VideoComposer AL cINN CogVideo Make-A-Video ModelScopeT2V
Builds on: Video Diffusion Models ModelScope Text-to-Video Stable Diffusion VideoComposer
Closest papers by meaning
- Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model (NeurIPS 2024 · similarity 0.90)
- VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide (CVPR 2025 · similarity 0.90)
- Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition (ICLR 2024 · similarity 0.90)
- Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation (NeurIPS 2024 · similarity 0.89)
- Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search (NeurIPS 2025 · similarity 0.89)
- Emergent Temporal Correspondences from Video Diffusion Transformers (NeurIPS 2025 · similarity 0.89)
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