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Birth and Death of a Rose
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Generates temporally evolving 3D object intrinsics (geometry, albedo, roughness, metallic) of natural processes like a blooming rose by distilling pretrained 2D diffusion models guided by DINOv2-based Neural Templates.
Problem
Generating temporal object intrinsics (time-evolving geometry and materials of natural processes such as blooming or rotting) without 3D or 4D training data, where SDS-style 4D distillation suffers from view and temporal-state inconsistency.
Approach
Neural Templates map camera pose and timestamp to a Neural State Map (PCA-compressed DINOv2 features of a coarse dynamic mesh, refined by a consistency model) that conditions a ControlNet-style diffusion model, anchoring SDS gradients to specific viewpoints and temporal states; a hybrid K-Planes + NGP 4D field is optimized with a differentiable PBR renderer.
Key results
- In a user study with 95 participants over 7 natural processes, the proposed method was ranked first in 76.84% of cases for concept alignment and 61.05% for motion realism, versus at most 21.95% for baselines.
- Visual quality CLIP score of 30.83 for the proposed method versus 30.11 for 4D-fy, 29.65 for DG4D, and 28.01 for STAG4D.
- Ablations show that removing Neural Template guidance causes 3D inconsistency and tiny motion, and K-Planes or Tensor4D alone over-smooth while NGP arrays lose temporal consistency.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Ours instead of STAG4D | 4D generation on 7 natural processes · 7 natural processes | CLIP score (user-study first-place rate): 16.69% -> 76.84% | Helped |
| Use Ours instead of STAG4D | 4D generation on 7 natural processes · 7 natural processes | Motion alignment: 13.83% -> 69.77% | Helped |
| Use Ours instead of 4D-Fy | 4D generation on 7 natural processes · 7 natural processes | Motion realism: 21.95% -> 61.05% | Helped |
| Use Ours instead of 4D-Fy | 4D generation on 7 natural processes · 7 natural processes | Visual quality: 30.11 -> 30.83 | Helped |
| Add consistency-model refinement of raw renderings (ablation) | Neural Template construction · Neural State Map generation | Neural State Map quality | Helped |
| Add Neural Template guidance (ablation) | 4D object intrinsics generation · Natural-process generation | 3D consistency | Helped |
| Add Neural Template guidance (ablation) | 4D object intrinsics generation · Natural-process generation | Motion | Helped |
| Replace the hybrid 4D representation with K-Planes (ablation) | 4D object intrinsics generation · Natural-process generation | Fine-detail quality | Hurt |
| Replace the hybrid 4D representation with Tensor4D (ablation) | 4D object intrinsics generation · Natural-process generation | Fine-detail quality | Hurt |
| Replace the hybrid 4D representation with an array of NGP encodings (ablation) | 4D object intrinsics generation · Natural-process generation | Temporal consistency | Hurt |
Surprising findings
- Existing 4D generation methods (4D-fy, DreamGaussian4D) tend to produce very small or unrealistic motion rather than the large temporal state changes required.
Limitations
- Evaluation is limited to 7 natural phenomena and a user study, with no standard benchmark.
- Relies on a coarse dynamic mesh from an off-the-shelf video diffusion model and 4D reconstruction, which may fail for rarely seen objects.
- Baselines were adapted to the task, and fidelity of the rendered materials is not quantitatively measured.
Experimental setup
Datasets: manually collected time-lapse videos synthetic image-DINOv2 feature pairs generated via image API
Benchmarks: user study on 7 natural phenomena CLIP score on generated renderings
Metrics: CLIP score user preference rate (concept alignment, motion realism, vis
Baselines: 4D-fy DreamGaussian4D STAG4D Fantasia3D
Builds on: Score Distillation Sampling ControlNet DINOv2 K-Planes Neural Graphical Primitives
Closest papers by meaning
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- Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields (CVPR 2024 · similarity 0.81)
- Neural Gaffer: Relighting Any Object via Diffusion (NeurIPS 2024 · similarity 0.81)
- Single Mesh Diffusion Models with Field Latents for Texture Generation (CVPR 2024 · similarity 0.81)
- ROGR: Relightable 3D Objects using Generative Relighting (NeurIPS 2025 · similarity 0.81)
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