# DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling

arXiv: https://arxiv.org/abs/2311.17082 · HTML: https://mlsanity.com/paper/2311.17082/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 36 of its 50 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 36)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [3D Shape Generation and Completion through Point-Voxel Diffusion](https://mlsanity.com/paper/2104.03670/) (2021)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://mlsanity.com/paper/2306.17843/) (2023)
- [Blockwise Parallel Decoding for Deep Autoregressive Models](https://mlsanity.com/paper/1811.03115/) (2018)
- [CLIP-Mesh: Generating textured meshes from text using pretrained image-text models](https://mlsanity.com/paper/2203.13333/) (2022)
- [3D Neural Field Generation using Triplane Diffusion](https://mlsanity.com/paper/2211.16677/) (2022)
- [The Role of ImageNet Classes in Fr'echet Inception Distance](https://mlsanity.com/paper/2203.06026/) (2022)
- [Cones: Concept Neurons in Diffusion Models for Customized Generation](https://mlsanity.com/paper/2303.05125/) (2023)
- [TextMesh: Generation of Realistic 3D Meshes From Text Prompts](https://mlsanity.com/paper/2304.12439/) (2023)
- [Parallel Sampling of Diffusion Models](https://mlsanity.com/paper/2305.16317/) (2023)

## Built on by (most cited first, 20 of 20)

- [SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency](https://mlsanity.com/paper/2407.17470/) (2024)
- [SV4D 2.0: Enhancing Spatio-Temporal Consistency in Multi-View Video Diffusion for High-Quality 4D Generation](https://mlsanity.com/paper/2503.16396/) (2025)
- [LLMs Meet Multimodal Generation and Editing: A Survey](https://mlsanity.com/paper/2405.19334/) (2024)
- [ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation](https://mlsanity.com/paper/2407.02040/) (2024)
- [Collaborative Control for Geometry-Conditioned PBR Image Generation](https://mlsanity.com/paper/2402.05919/) (2024)
- [A Survey On Text-to-3D Contents Generation In The Wild](https://mlsanity.com/paper/2405.09431/) (2024)
- [Text-to-3D Shape Generation](https://mlsanity.com/paper/2403.13289/) (2024)
- [CharacterShot: Controllable and Consistent 4D Character Animation](https://mlsanity.com/paper/2508.07409/) (2025)
- [Splat4D: Diffusion-Enhanced 4D Gaussian Splatting for Temporally and Spatially Consistent Content Creation](https://mlsanity.com/paper/2508.07557/) (2025)
- [A General Framework to Boost 3D GS Initialization for Text-to-3D Generation by Lexical Richness](https://mlsanity.com/paper/2408.01269/) (2024)
- [Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video Generation](https://mlsanity.com/paper/2509.10687/) (2025)
- [FB-4D: Spatial-Temporal Coherent Dynamic 3D Content Generation with Feature Banks](https://mlsanity.com/paper/2503.20784/) (2025)
- [4DVD: Cascaded Dense-view Video Diffusion Model for High-quality 4D Content Generation](https://mlsanity.com/paper/2508.04467/) (2025)
- [GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer](https://mlsanity.com/paper/2510.16136/) (2025)
- [Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference](https://mlsanity.com/paper/2603.01594/) (2026)
- [Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation](https://mlsanity.com/paper/2312.11774/) (2023)
- [CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers](https://mlsanity.com/paper/2507.15260/) (2025)
- [Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation](https://mlsanity.com/paper/2507.09748/) (2025)
- [ForgeDreamer: Industrial Text-to-3D Generation with Multi-Expert LoRA and Cross-View Hypergraph](https://mlsanity.com/paper/2603.09266/) (2026)
- [Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation](https://mlsanity.com/paper/2607.12752/) (2026)

## Abstract (arXiv)

Recent methods such as Score Distillation Sampling (SDS) and Variational Score Distillation (VSD) using 2D diffusion models for text-to-3D generation have demonstrated impressive generation quality. However, the long generation time of such algorithms significantly degrades the user experience. To tackle this problem, we propose DreamPropeller, a drop-in acceleration algorithm that can be wrapped around any existing text-to-3D generation pipeline based on score distillation. Our framework generalizes Picard iterations, a classical algorithm for parallel sampling an ODE path, and can account for non-ODE paths such as momentum-based gradient updates and changes in dimensions during the optimization process as in many cases of 3D generation. We show that our algorithm trades parallel compute for wallclock time and empirically achieves up to 4.7x speedup with a negligible drop in generation quality for all tested frameworks.