Research map / 3D vision / 3D generation
Text-To-4D Dynamic Scene Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MAV3D rather than frame-wise Make-A-Video-to-3D baselines | Text-to-4D generation · Text-to-4D | R-Precision: 8.8, 10.6, 6.2 -> 83.7 | Helped |
| Use static scene pre-training before dynamic optimization (ablation) | MAV3D · Text-to-4D | R-Precision: 63.5 -> 83.7 | Helped |
| Add super-resolution fine-tuning (ablation) | MAV3D · Text-to-4D | video quality | Helped |
| Use dynamic camera trajectories rather than a fixed camera (ablation) | MAV3D · Text-to-4D | amount of motion | Helped |
| Add Gaussian annealing during dynamic optimization (ablation) | MAV3D · Text-to-4D | motion realism | Helped |
| Replace HexPlane with D-NeRF (ablation) | MAV3D · Text-to-4D | overall quality | Hurt |
| Replace the static NeRF backbone with Instant-NGP (ablation) | MAV3D · Text-to-4D | human preference | Hurt |
| Increase static pre-training beyond 2000 iterations (ablation) | MAV3D · Text-to-4D | scene quality | No clear effect |
Closest papers by meaning
- 4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models (NeurIPS 2024 · similarity 0.91)
- 4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling (CVPR 2024 · similarity 0.90)
- A Unified Approach for Text- and Image-guided 4D Scene Generation (CVPR 2024 · similarity 0.90)
- SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency (ICLR 2025 · similarity 0.89)
- Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model (ICLR 2024 · similarity 0.89)
- 3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation (CVPR 2024 · similarity 0.88)
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