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Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion
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Instruct 4D-to-4D edits 4D (dynamic) NeRF scenes by treating them as pseudo-3D scenes of video pseudo-views, combining an anchor-aware Instruct-Pix2Pix, optical-flow sliding-window propagation, and depth-warped key-view propagation within iterative dataset updates.
Problem
Instruction-guided editing of 4D dynamic scenes with 2D diffusion models suffers from spatial and temporal inconsistency because edits are applied frame by frame and 4D NeRFs do not tie the same object across frames to the same parameters.
Approach
Regard a 4D scene as a pseudo-3D scene whose pseudo-views are videos; decouple into temporal-consistent video editing per pseudo-view (anchor-aware IP2P with 1x3x3 pseudo-3D convs, RAFT optical-flow sliding window) and spatial propagation from key pseudo-views via depth-based warping, then iteratively update the edited dataset used to fit NeRFPlayer.
Key results
- On coffee martini quantitative comparison, average PSNR 19.67 vs 14.11 for IN2N-4D; SSIM 0.635 vs 0.457.
- Ablation CLIP similarity: full method 0.3085 vs IN2N-4D 0.2790 and Video Editing (FateZero) 0.2631.
- Pipeline completes large 4D scene editing (20 views, hundreds of frames) in hours, with IN2N-4D taking 24x longer and failing to converge.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Instruct 4D-to-4D instead of IN2N-4D | 4D scene editing · Coffee martini, Van Gogh | PSNR: 17.43 -> 23.62 | Helped |
| Use Instruct 4D-to-4D instead of IN2N-4D | 4D scene editing · Coffee martini, Van Gogh | SSIM: 0.645 -> 0.820 | Helped |
| Use Instruct 4D-to-4D instead of IN2N-4D | 4D scene editing · Coffee martini, Van Gogh | LPIPS Alex: 0.466 -> 0.220 | Helped |
| Use Instruct 4D-to-4D instead of IN2N-4D | 4D scene editing · Coffee martini, Van Gogh | LPIPS VGG: 0.573 -> 0.349 | Helped |
| Replace FateZero video editing with the full Instruct 4D-to-4D pipeline (ablation) | Instruct 4D-to-4D · Coffee martini, Van Gogh | CLIP similarity: 0.2631 -> 0.3085 | Helped |
| Add optical-flow guidance (ablation) | Instruct 4D-to-4D · Coffee martini, Van Gogh | CLIP similarity: 0.2792 -> 0.3085 | Helped |
| Replace one-time propagation with iterative editing (ablation) | Instruct 4D-to-4D · Coffee martini, Van Gogh | CLIP similarity: 0.2876 -> 0.3085 | Helped |
Surprising findings
- Video editing with FateZero plus 3D warping did not beat the IN2N-4D baseline on CLIP similarity.
- The original IN2N-4D baseline fails to converge to the indicated style even after 24x more time.
Limitations
- No prior 4D instruction-guided editing baseline exists, so comparison is limited to a naive IN2N-4D extension.
- Quantitative evaluation is on a single multi-camera scene and uses generated-image metrics rather than ground truth.
- Reliance on optical flow and depth warping may fail on occlusions; attention may mis-associate objects across views.
Experimental setup
Datasets: DyCheck HyperNeRF DyNeRF/N3DV
Benchmarks: DyNeRF coffee martini (multi-camera)
Metrics: PSNR SSIM LPIPS CLIP similarity
Baselines: IN2N-4D FateZero
Builds on: Instruct-Pix2Pix Instruct-NeRF2NeRF ViCA-NeRF Tune-A-Video NeRFPlayer
Closest papers by meaning
- Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation (CVPR 2025 · similarity 0.91)
- Control4D: Efficient 4D Portrait Editing with Text (CVPR 2024 · similarity 0.91)
- CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion (CVPR 2025 · similarity 0.91)
- ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing (CVPR 2024 · similarity 0.88)
- Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding (NeurIPS 2025 · similarity 0.88)
- EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting (CVPR 2025 · similarity 0.88)
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