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DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation
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Proposes DreamTime (TP-SDS), a non-increasing, weighted timestep schedule for score distillation sampling that aligns 3D optimization with DDPM sampling, improving convergence speed, quality and diversity of diffusion-guided 3D generation.
Problem
Score distillation sampling (SDS) for text-to-3D and image-to-3D generation uses uniformly random diffusion timesteps, causing slow convergence, poor shape/texture/attribute fidelity, and low diversity.
Approach
Replace uniform timestep sampling in SDS with a monotonically non-increasing timestep schedule, derived from a weight function combining the diffusion SNR-based term and a Gaussian prior over timesteps, so large-t coarse structure is learned first and small-t detail later.
Key results
- TP-SDS requires roughly 75% fewer optimization steps than uniform-t SDS to reach comparable results, per the authors' empirical observation.
- On 2D R-Precision with 153 COCO prompts, TP-SDS shows a steeper growth curve than SDS across CLIP B/32, B/16 and L/14.
- User preference studies across DreamFusion, TextMesh, Fantasia3D, Magic3D and Zero-1-to-3 report consistently higher preference for TP-SDS.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use weighted non-increasing timestep sampling instead of uniform timestep sampling | SDS for diffusion-guided generation · 2D generation, object-centric COCO validation prompts | optimization steps to reach the same R-Precision: ~75% fewer | Helped |
| Use weighted non-increasing timestep sampling instead of uniform timestep sampling | DreamFusion and other diffusion-guided 3D generators · Text-to-3D generation | visual generation quality | Helped |
| Use weighted non-increasing timestep sampling instead of uniform timestep sampling | DreamFusion · Text-to-3D generation with different random seeds | generation diversity | Helped |
| Use a linearly decreasing timestep schedule (ablation) | TP-SDS · Text-to-3D generation, “Chichen Itza, aerial view” | visual generation quality | Hurt |
| Use a linearly decreasing timestep schedule truncated to a minimum timestep of 200 (ablation) | TP-SDS · Text-to-3D generation, “Chichen Itza, aerial view” | visual generation quality | Mixed |
| Use only the Gaussian prior weight W_p(t) (ablation) | TP-SDS · Text-to-3D generation, “Chichen Itza, aerial view” | visual generation quality | Hurt |
| Use only the diffusion-derived prior weight W_d(t) (ablation) | TP-SDS · Text-to-3D generation, “Chichen Itza, aerial view” | visual generation quality | Hurt |
| Increase the prior-weight parameter s (ablation) | TP-SDS · Text-to-3D generation | visual generation quality | Hurt |
Surprising findings
- A naive linearly decreasing timestep schedule causes severe artifacts and training divergence, so a carefully weighted schedule is needed.
- Using small timesteps (e.g., t=100) with SDS makes gradient variance too high to produce a 3D model, while very large t (e.g., 900) lacks local detail.
Limitations
- Introduces extra hyper-parameters m and s for the Gaussian prior, which require tuning (rule of thumb m near T/2).
- Potential inheritance of social biases from Stable Diffusion training data.
- Evaluation of quality and diversity is largely qualitative and based on user preference studies.
Experimental setup
Datasets: COCO validation set (153 object-centric prompts)
Benchmarks: COCO validation object-centric prompts (R-Precision)
Metrics: R-Precision user preference convergence steps (qualitative) diversity (qualitative)
Baselines: DreamFusion (SDS) TextMesh Fantasia3D Magic3D Zero-1-to-3 HiFA ProlificDreamer (VSD)
Builds on: DreamFusion score distillation sampling threestudio DDPM ProlificDreamer
Closest papers by meaning
- DreamFlow: High-quality text-to-3D generation by Approximating Probability Flow (ICLR 2024 · similarity 0.92)
- MVDream: Multi-view Diffusion for 3D Generation (ICLR 2024 · similarity 0.91)
- Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation (ICLR 2024 · similarity 0.91)
- Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models (ICLR 2025 · similarity 0.90)
- HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance (ICLR 2024 · similarity 0.90)
- EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Priors (CVPR 2024 · similarity 0.90)
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