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Animate3D: Animating Any 3D Model with Multi-view Video Diffusion
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Animate3D animates any static 3D model by training a multi-view video diffusion model (MV-VDM, built on MVDream and AnimateDiff with a spatiotemporal attention module and multi-view image conditioning) on a 115K-animation multi-view video dataset, then optimizing a 4DGS via reconstruction, 4D-SDS and ARAP losses.
Problem
Animating existing off-the-shelf 3D assets while preserving their multi-view appearance and maintaining spatiotemporal consistency, which prior distillation-based 4D generation methods (text- or single-view-conditioned) fail to do.
Approach
A multi-view video diffusion foundation model conditioned on multi-view renderings of a static 3D object, combining a parallel spatial (multi-view 3D) and temporal (motion module) attention block with an MV2V-Adapter for image conditioning, trained on a large multi-view animation dataset; the resulting multi-view videos supervise 4DGS via reconstruction plus 4D-SDS.
Key results
- Animate3D achieves I2V Subject 0.983 and Aesthetic Quality 0.552 versus 0.910 and 0.491 for DreamGaussian4D in the 4D comparison.
- In the user study (20 participants), Animate3D scores 4.735 on alignment with 3D object versus 3.316 for DreamGaussian4D and 1.553 for 4Dfy (Gau.).
- Ablation shows removing spatiotemporal attention lowers I2V Subject from 0.935 to 0.915 for MV-VDM.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Animate3D instead of DG4D | 4D generation · 4D generation | I2V: 0.910 -> 0.983 | Helped |
| use Animate3D instead of DG4D | 4D generation · 4D generation | Motion Smoothness: 0.990 -> 0.993 | No clear effect |
| use Animate3D instead of DG4D | 4D generation · 4D generation | Aesthetic Quality: 0.491 -> 0.552 | Helped |
| add spatiotemporal attention (ablation) | MV-VDM · MV-VDM ablation | I2V: 0.915 -> 0.935 | Helped |
| add pre-trained video diffusion weights (ablation) | MV-VDM · MV-VDM ablation | Motion Smoothness: 0.981 -> 0.988 | Helped |
| add spatiotemporal attention (ablation) | MV-VDM · MV-VDM ablation | Dynamic Degree: 0.958 -> 0.710 | Mixed |
| add 4D-SDS loss (ablation) | Animate3D 4D generation · 4D generation ablation | Aesthetic Quality: 0.551 -> 0.552 | No clear effect |
| add 4D-SDS loss (ablation) | Animate3D 4D generation · 4D generation ablation | Dynamic Degree: 0.404 -> 0.388 | Mixed |
| add ARAP loss (ablation) | Animate3D 4D generation · 4D generation ablation | Aesthetic Quality: 0.509 -> 0.552 | Helped |
| add ARAP loss (ablation) | Animate3D 4D generation · 4D generation ablation | I2V: 0.968 -> 0.983 | Helped |
| add ARAP loss (ablation) | Animate3D 4D generation · 4D generation ablation | Motion Smoothness: 0.992 -> 0.993 | Helped |
| add ARAP loss (ablation) | Animate3D 4D generation · 4D generation ablation | Dynamic Degree: 0.360 -> 0.388 | Mixed |
Surprising findings
- Dynamic Degree is higher for some ablated variants (e.g., w/o S.T. Att. at 0.958), which the authors attribute to unstable failure cases rather than good motion.
Limitations
- Animating an existing 3D object takes about 40 minutes per object.
- Trade-off between temporal coherence and motion amplitude in generated multi-view videos.
- Domain gap between synthetic training data and real-world test data limits realistic scene animation.
Experimental setup
Datasets: MV-Video Objaverse Sketchfab
Benchmarks: VBench I2V metrics on held-out MV-Video animations 4D generation evaluation set of 20 objects
Metrics: I2V Subject Motion Smoothness Dynamic Degree Aesthetic Quality user study scores
Baselines: 4Dfy DreamGaussian4D
Builds on: MVDream AnimateDiff I2V-Adapter 4D Gaussian Splatting IP-Adapter
Closest papers by meaning
- 4Diffusion: Multi-view Video Diffusion Model for 4D Generation (NeurIPS 2024 · similarity 0.90)
- Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models (ICLR 2025 · similarity 0.89)
- Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models (NeurIPS 2024 · similarity 0.89)
- SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency (ICLR 2025 · similarity 0.89)
- Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models (CVPR 2024 · similarity 0.88)
- One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion (CVPR 2024 · similarity 0.88)
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