Probability-Flow Distillation: Exact Wasserstein Gradient Flow for High-Fidelity 3D Generation
For 3D content creation from text, this work addresses the mode collapse problem in score distillation, enabling higher-fidelity generation.
The paper tackles mode collapse in text-to-3D generation from 2D diffusion priors. Probability-Flow Distillation (PFD) achieves improved quality and high-fidelity 3D assets compared to existing methods like SDS and SDI.
Score Distillation Sampling (SDS) and its variants have been widely used for text-to-3D generation by distilling 2D image diffusion priors. However, the standard SDS objective is prone to severe mode collapse, frequently yielding over-smoothed and over-saturated results. Although recent advancements, such as Score Distillation via Inversion (SDI), mitigate these artifacts and produce visually sharper models, they ultimately fail to faithfully capture the full target distribution. In this work, we show that the bottleneck limiting the sampling capacity of SDI stems from its reliance on the posterior mean estimator, which is mathematically equivalent to a single-step Euler approximation of the deterministic reverse DDIM trajectory. To address this, we propose a naturally motivated extension termed Probability-Flow Distillation (PFD). We establish that PFD corresponds exactly to a Wasserstein gradient flow, thereby inducing principled distribution-matching dynamics. Finally, we show that PFD can synthesize 3D assets with fine-grained, high-fidelity details and achieve improved quality compared to existing methods.