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Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video
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Restage4D reanimates a single monocular video's deformable 4D Gaussian reconstruction using a text-driven image-to-video synthetic driving video, jointly optimized with the time-reversed base video plus occlusion-aware rigidity and disocclusion backtracing.
Problem
Generated (image-to-video) driving videos for reanimating a 4D scene contain geometric artifacts, implausible motion, and occlusions, so reconstructing 4D content from them is physically inconsistent.
Approach
Rewind the base video and concatenate it with the synthetic driving video so both share a canonical frame and motion coefficients in a Shape-of-Motion representation; add an occlusion-aware rigidity loss and a disocclusion backtracing step to propagate geometry supervision from the real video into occluded/disoccluded regions.
Key results
- On 20 DAVIS sequences, joint training plus ARAP improves PSNR from 26.71 to 27.12, OCV CLIP from 81.68 to 85.40, and volume consistency from 7.41 to 10.32 over the baseline.
- Edge consistency rises from 12.66 (baseline) to 16.28 with joint plus ARAP.
- Restage4D reduces 3D tracking L1 error on PointOdyssey for occluded regions compared to the baseline without rigidity loss (quantitative table referenced).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | PSNR: +0.19 | Helped |
| Add ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | OCV CLIP: +0.97 | Helped |
| Add ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | Vol. Consist: +0.51 | Helped |
| Add ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | Edge Consist: +0.98 | Helped |
| Add video-rewinding joint training (ablation) | Restage4D baseline · DAVIS (20 sequences) | PSNR: –0.13 | Hurt |
| Add video-rewinding joint training (ablation) | Restage4D baseline · DAVIS (20 sequences) | OCV CLIP: +0.61 | Helped |
| Add video-rewinding joint training (ablation) | Restage4D baseline · DAVIS (20 sequences) | Vol. Consist: +1.09 | Helped |
| Add video-rewinding joint training (ablation) | Restage4D baseline · DAVIS (20 sequences) | Edge Consist: +0.58 | Helped |
| Add both video-rewinding joint training and ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | PSNR: +0.41 | Helped |
| Add both video-rewinding joint training and ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | OCV CLIP: +3.72 | Helped |
| Add both video-rewinding joint training and ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | Vol. Consist: +2.91 | Helped |
| Add both video-rewinding joint training and ARAP rigidity loss (ablation) | Restage4D baseline · DAVIS (20 sequences) | Edge Consist: +3.62 | Helped |
Surprising findings
- Joint training alone slightly decreases PSNR (26.71 to 26.58) while improving geometry metrics.
- The paper's own lemma relies on smoothness assumptions and is loosely stated; its proof is heuristic.
Limitations
- Performance depends on the quality of the generated driving video; severe artifacts or unrealistic motion limit recovery.
- Assumes the input object stays temporally consistent across sequences, which may fail for highly deformable or textureless objects.
- Evaluation relies on custom metrics and a 20-sequence DAVIS subset.
Experimental setup
Datasets: DAVIS PointOdyssey
Benchmarks: DAVIS PointOdyssey
Metrics: PSNR CLIP similarity for occlusion views (OCV CLIP) volume consistency edge length consistency L1 3D trajectory error
Baselines: Baseline without rigidity/joint training (Shape-of-Motion-style reconstruction) SC-GS D-MiSo
Builds on: Shape-of-Motion As-Rigid-As-Possible Gaussian Splatting TAPIR Segment Anything
Closest papers by meaning
- Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding (NeurIPS 2025 · similarity 0.88)
- 4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models (NeurIPS 2024 · similarity 0.87)
- DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos (NeurIPS 2024 · similarity 0.87)
- Robust Multi-Object 4D Generation for In-the-wild Videos (CVPR 2025 · similarity 0.87)
- CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models (CVPR 2025 · similarity 0.87)
- Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video (ICLR 2024 · similarity 0.86)
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