# HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation

arXiv: https://arxiv.org/abs/2307.16183 · HTML: https://mlsanity.com/paper/2307.16183/

## Key facts

- Cited by 27 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 25 of its 31 references that are in the map.
- First posted on arXiv 30 Jul 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, 25 of 25)

- [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)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Pushing the Boundaries of View Extrapolation with Multiplane Images](https://mlsanity.com/paper/1905.00413/) (2019)
- [Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality](https://mlsanity.com/paper/2202.05830/) (2022)
- [Neural Volumes: Learning Dynamic Renderable Volumes from Images](https://mlsanity.com/paper/1906.07751/) (2019)
- [DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder](https://mlsanity.com/paper/2206.00386/) (2022)

## Built on by (most cited first, 27 of 27)

- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets](https://mlsanity.com/paper/2406.13897/) (2024)
- [GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models](https://mlsanity.com/paper/2310.08529/) (2023)
- [RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D](https://mlsanity.com/paper/2311.16918/) (2023)
- [SPAD : Spatially Aware Multiview Diffusers](https://mlsanity.com/paper/2402.05235/) (2024)
- [Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text](https://mlsanity.com/paper/2406.17601/) (2024)
- [Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video](https://mlsanity.com/paper/2401.08742/) (2024)
- [HiFi-123: Towards High-fidelity One Image to 3D Content Generation](https://mlsanity.com/paper/2310.06744/) (2023)
- [Disentangled Clothed Avatar Generation from Text Descriptions](https://mlsanity.com/paper/2312.05295/) (2023)
- [Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting](https://mlsanity.com/paper/2312.13271/) (2023)
- [Diffusion Time-step Curriculum for One Image to 3D Generation](https://mlsanity.com/paper/2404.04562/) (2024)
- [Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping](https://mlsanity.com/paper/2310.12474/) (2023)
- [DPoser: Diffusion Model as Robust 3D Human Pose Prior](https://mlsanity.com/paper/2312.05541/) (2023)
- [Efficient-NeRF2NeRF: Streamlining Text-Driven 3D Editing with Multiview Correspondence-Enhanced Diffusion Models](https://mlsanity.com/paper/2312.08563/) (2023)
- [DreamCar: Leveraging Car-specific Prior for in-the-wild 3D Car Reconstruction](https://mlsanity.com/paper/2407.16988/) (2024)
- [Envision3D: One Image to 3D with Anchor Views Interpolation](https://mlsanity.com/paper/2403.08902/) (2024)
- [Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion](https://mlsanity.com/paper/2405.09874/) (2024)
- [Wonder3D++: Cross-domain Diffusion for High-fidelity 3D Generation from a Single Image](https://mlsanity.com/paper/2511.01767/) (2025)
- [PlacidDreamer: Advancing Harmony in Text-to-3D Generation](https://mlsanity.com/paper/2407.13976/) (2024)
- [Direct and Explicit 3D Generation from a Single Image](https://mlsanity.com/paper/2411.10947/) (2024)
- [DMS:Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimation](https://mlsanity.com/paper/2508.13091/) (2025)
- [Few-step Flow for 3D Generation via Marginal-Data Transport Distillation](https://mlsanity.com/paper/2509.04406/) (2025)
- [MVRoom: Controllable 3D Indoor Scene Generation with Multi-View Diffusion Models](https://mlsanity.com/paper/2512.04248/) (2025)
- [Generative AI for Accessible and Inclusive Extended Reality](https://mlsanity.com/paper/2410.23803/) (2024)
- [ESCT3D: Efficient and Selectively Controllable Text-Driven 3D Content Generation with Gaussian Splatting](https://mlsanity.com/paper/2504.10316/) (2025)
- [LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework](https://mlsanity.com/paper/2505.24245/) (2025)

## Abstract (arXiv)

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x 512) renderings can lead to the production of high-quality 3D models using latent diffusion priors. To enable rendering at even higher resolutions, which has the potential to further augment the quality and detail of the models, we propose a novel approach that combines multiple noise estimation processes with a pretrained 2D diffusion prior. Distinct from the Bar-Tal et al.s' study which binds multiple denoised results to generate images from texts, our approach integrates the computation of scoring distillation losses such as SDS loss and VSD loss which are essential techniques for the 3D content generation with 2D diffusion priors. We experimentally evaluated the proposed approach. The results show that the proposed approach can generate high-quality details compared to the baselines.