Summary of Consistent3d: Towards Consistent High-fidelity Text-to-3d Generation with Deterministic Sampling Prior, by Zike Wu et al.
Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior
by Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Consistent3D, a novel method for text-to-3D generation that addresses the limitations of Score Distillation Sampling (SDS) and its variants. SDS is prone to geometry collapse and poor textures due to its reliance on stochastic differential equation (SDE) trajectory sampling. The authors analyze SDS and find that it corresponds to SDE trajectory sampling, but this randomness can lead to unpredictable and less noisy samples. To overcome this issue, the proposed Consistent3D method utilizes ordinary differential equation (ODE) deterministic sampling prior for text-to-3D generation. The ODE is used to estimate the desired 3D score function by a pre-trained 2D diffusion model, which is then employed to generate two adjacent samples and distill the deterministic prior into the 3D model. Experimental results demonstrate the efficacy of Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic 3D models from text descriptions. This is what scientists have been trying to achieve with a method called Score Distillation Sampling (SDS). However, SDS has its limitations and can produce poor textures or geometry collapse. To solve this problem, researchers developed a new approach called Consistent3D. It uses a different way of sampling 3D models based on ordinary differential equations (ODEs), which are more predictable than the original method. The result is higher-quality 3D objects that look more realistic and varied. This technology has many potential applications, such as creating virtual environments for gaming or training robots to perform tasks. |
Keywords
* Artificial intelligence * Diffusion model * Distillation
