# Consistent Flow Distillation for Text-to-3D Generation

arXiv: https://arxiv.org/abs/2501.05445 · HTML: https://mlsanity.com/paper/2501.05445/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 17 of 17)

- [A Survey on Pre-Trained Diffusion Model Distillations](https://mlsanity.com/paper/2502.08364/) (2025)
- [On Equivariance and Fast Sampling in Video Diffusion Models Trained with Warped Noise](https://mlsanity.com/paper/2504.09789/) (2025)
- [Coupled Diffusion Sampling for Training-Free Multi-View Image Editing](https://mlsanity.com/paper/2510.14981/) (2025)
- [Generative 3D Gaussians with Learned Density Control](https://mlsanity.com/paper/2605.16355/) (2026)
- [Text-to-3D Generation using Jensen-Shannon Score Distillation](https://mlsanity.com/paper/2503.10660/) (2025)
- [Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling](https://mlsanity.com/paper/2510.23605/) (2025)
- [Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference](https://mlsanity.com/paper/2603.01594/) (2026)
- [Block3D: Efficient Text-to-3D Generation via Block-Wise Diffusion](https://mlsanity.com/paper/2608.19567/) (2026)
- [Mean-Shift Distillation for Diffusion Mode Seeking](https://mlsanity.com/paper/2502.15989/) (2025)
- [Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning](https://mlsanity.com/paper/2505.11777/) (2025)
- [SceneLCM: End-to-End Layout-Guided Interactive Indoor Scene Generation with Latent Consistency Model](https://mlsanity.com/paper/2506.07091/) (2025)
- [Target-Balanced Score Distillation](https://mlsanity.com/paper/2511.11710/) (2025)
- [HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance](https://mlsanity.com/paper/2604.04425/) (2026)
- [FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation](https://mlsanity.com/paper/2604.09989/) (2026)
- [CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation](https://mlsanity.com/paper/2605.20872/) (2026)
- [Variance Reduction on the Camera Axis: Multi-View Score Distillation for 3D](https://mlsanity.com/paper/2606.29964/) (2026)
- [SceneHI: High-Resolution 3D-Consistent Scene Texturing with Controllable Illumination](https://mlsanity.com/paper/2609.10363/) (2026)

## Abstract (arXiv)

Score Distillation Sampling (SDS) has made significant strides in distilling image-generative models for 3D generation. However, its maximum-likelihood-seeking behavior often leads to degraded visual quality and diversity, limiting its effectiveness in 3D applications. In this work, we propose Consistent Flow Distillation (CFD), which addresses these limitations. We begin by leveraging the gradient of the diffusion ODE or SDE sampling process to guide the 3D generation. From the gradient-based sampling perspective, we find that the consistency of 2D image flows across different viewpoints is important for high-quality 3D generation. To achieve this, we introduce multi-view consistent Gaussian noise on the 3D object, which can be rendered from various viewpoints to compute the flow gradient. Our experiments demonstrate that CFD, through consistent flows, significantly outperforms previous methods in text-to-3D generation.