# ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

**Authors:** Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu  
**arXiv:** [2305.16213](https://arxiv.org/abs/2305.16213)  
**Date:** 2023-05-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.16213v2/

## Key points

- Authors Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu introduce a novel approach to address limitations of score distillation sampling (SDS) in text-to-3D generation.
- Key innovation: Modeling the 3D parameter as a random variable instead of a constant as done in SDS.
- Development of variational score distillation (VSD), a particle-based variational framework to tackle issues like over-saturation, over-smoothing, and low diversity in generated samples.
- VSD proves effective with various configuration weights by employing ancestral sampling from diffusion models to enhance sample diversity and improve overall sample quality.
- Enhancements in design space for text-to-3D generation include optimizations related to distillation time schedule and density initialization.
- ProlificDreamer showcases impressive capabilities in generating high rendering resolution (512x512) outputs and high-fidelity Neural Radiance Fields (NeRF) with intricate structures and complex visual effects like smoke and drops.
- Fine-tuning meshes initialized from NeRF using VSD results in 3D models with meticulous details and photorealistic qualities.

## Summary

In their paper titled "ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation," authors Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu introduce a novel approach to address the limitations of score distillation sampling (SDS) in text-to-3D generation. SDS has shown promise in leveraging pretrained text-to-image diffusion models but has been plagued by issues such as over-saturation, over-smoothing, and low diversity in generated samples. The key innovation proposed by the authors is the modeling of the 3D parameter as a random variable rather than a constant as done in SDS. This leads to the development of variational score distillation (VSD), a particle-based variational framework that aims to tackle these challenges. The authors demonstrate that SDS can be viewed as a special case of VSD but often produces subpar samples across different configuration weights. In contrast, VSD proves to be effective with various configuration weights by employing ancestral sampling from diffusion models. It not only enhances sample diversity but also improves overall sample quality when using a common configuration weight of $7.5. Additionally, the authors present several enhancements in the design space for text-to-3D generation including optimizations related to distillation time schedule and density initialization. The proposed approach, dubbed ProlificDreamer, showcases impressive capabilities in generating high rendering resolution (512x512) outputs and high-fidelity Neural Radiance Fields (NeRF) with intricate structures and complex visual effects like smoke and drops. By fine-tuning meshes initialized from NeRF using VSD, the generated 3D models exhibit meticulous details and photorealistic qualities. This research was presented at NeurIPS 2023 as a Spotlight paper and offers valuable insights into advancing text-to-3D generation techniques. More information about ProlificDreamer can be found on the project page along with access to relevant codes for further exploration and implementation.