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BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
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Proposes BIVDiff, a training-free framework that generates frames with an image diffusion model, applies Mixed Inversion, and temporally smooths them with a text-to-video diffusion model for general video synthesis tasks.
Problem
Video synthesis tasks (controllable generation, editing, inpainting, outpainting) need costly video model training or per-video tuning, and training-free frame-wise image adaptations suffer from temporal flicker and are task-specific.
Approach
Decouple image and video models: run a task-specific image diffusion model frame by frame, then DDIM-invert the result with both image and video models, mix the two inverted latents with a weight alpha, and feed them into a pretrained text-to-video diffusion model for temporal smoothing, with no weight updates.
Key results
- On controllable generation, BIVDiff reaches 93.50 frame consistency for the Tune-A-Video comparison and 92.67 vs 91.69 (Text2Video-Zero) for depth control, with average user-study score 3.11 vs 2.96 for Text2Video-Zero.
- Sequential IDM-then-VDM bridging was preferred over alternate and fuse strategies in the user study (Fig. 7).
- Inference takes about 61-64 s per video, comparable to Text2Video-Zero and ControlVideo and much faster than Tune-A-Video (11 min + 26 s) and FateZero (221 s).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sequential bridging: frame-wise image generation, Mixed Inversion, then video temporal smoothing (ablation) | BIVDiff · Controllable video generation | Quality | Helped |
| Replace sequential bridging with alternating image- and video-model denoising (ablation) | BIVDiff · Controllable video generation | Temporal consistency | Hurt |
| Replace sequential bridging with simultaneous fused image- and video-model denoising (ablation) | BIVDiff · Controllable video generation | Temporal consistency | Hurt |
| Increase the Mixed Inversion ratio α (ablation) | BIVDiff with Prompt2Prompt · Video editing | Temporal consistency | Helped |
| Increase the Mixed Inversion ratio α (ablation) | BIVDiff with Prompt2Prompt · Video editing | Video quality | Hurt |
| Set α=0.25 for Mixed Inversion (ablation) | BIVDiff with Prompt2Prompt · Video editing | Video quality | Helped |
| Use BIVDiff rather than ControlVideo | DAVIS controllable video generation (depth control) · DAVIS | Frame Consistency: 92.63 -> 92.67 | Helped |
| Use BIVDiff rather than ControlVideo | DAVIS controllable video generation (depth control) · DAVIS | Avg.: 2.76 -> 3.11 | Helped |
| Use BIVDiff rather than Tune-A-Video | DAVIS video editing · DAVIS | Frame Consistency: 90.46 -> 93.50 | Helped |
| Use BIVDiff rather than Tune-A-Video | DAVIS video editing · DAVIS | Textual Alignment: 28.33 -> 26.16 | Hurt |
| Use BIVDiff rather than Tune-A-Video | DAVIS video editing · DAVIS | Avg.: 2.29 -> 2.65 | Helped |
Surprising findings
- VidRD fails to produce meaningful videos from highly correlated frame-wise DDIM-inverted latents, which motivated Mixed Inversion.
Limitations
- Mixing ratio alpha must be tuned per image model (1.0, 1.0, 0.25, 0.1 used), trading temporal smoothing against the video model's open generation ability.
- Evaluation is on a small DAVIS subset of 16 videos with 4 prompts each, and user study uses 25 raters.
Experimental setup
Datasets: DAVIS (via LOVEU-TGVE benchmark)
Benchmarks: LOVEU-TGVE DAVIS subset
Metrics: frame consistency (CLIP) textual alignment (CLIP) user study quality/fidelity/alignment inference time
Baselines: Text2Video-Zero FateZero Tune-A-Video ControlVideo ControlNet VidRD
Builds on: ControlNet Instruct Pix2Pix Prompt2Prompt VidRD Stable Diffusion Inpainting DDIM Inversion
Closest papers by meaning
- Hierarchical Patch Diffusion Models for High-Resolution Video Generation (CVPR 2024 · similarity 0.89)
- SF-V: Single Forward Video Generation Model (NeurIPS 2024 · similarity 0.89)
- OSV: One Step is Enough for High-Quality Image to Video Generation (CVPR 2025 · similarity 0.88)
- FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis (CVPR 2024 · similarity 0.88)
- From Slow Bidirectional to Fast Autoregressive Video Diffusion Models (CVPR 2025 · similarity 0.88)
- MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance (AAAI 2024 · similarity 0.88)
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