DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior

핵심 개념

DreamControl proposes a two-stage framework for text-to-3D generation, focusing on optimizing NeRF scenes as 3D self-prior and generating high-quality content with control-based score distillation.

초록

Introduction

Automatic 3D content generation gains attention in various fields.
Text-to-image diffusion models pave the way for controllable 3D generation.

Main Causes of Inconsistent 3D Generation

Viewpoint bias in 2D diffusion models leads to overfitting during optimization.
The Janus problem arises from inconsistent geometry in generated 3D models.

Proposed Solution: DreamControl Framework

Two-stage approach optimizes NeRF scenes as self-prior and refines objects with control-based score distillation.
Adaptive viewpoint sampling and boundary integrity metric ensure consistency in generated priors.

Key Contributions

Optimization of NeRF as a self-prior enhances geometry consistency and texture fidelity in 3D content generation.

Related Work

Methods split into 3D supervised and 2D lifting categories for text-to-3D generation tasks.

Experiments

DreamControl outperforms existing methods in geometry consistency, texture fidelity, and text relevance metrics.

User-Guided Generation & 3D Animation

DreamControl enables flexible user-guided generation and seamless binding with template skeletons for animation tasks.

통계

Neural radiance fields (NeRF) is trained to predict color RGB and density σ of sampling points supervised by total squared error between rendered and ground-truth pixel colors (Eq.1).
View-dependent prompts like "front view" are used to adjust viewpoint sampling distribution (Eq.5).
Boundary integrity metric calculates difference between density of valid pixels and boundary pixels to avoid overfitting (Eq.6).

인용구

"Generating high-quality 3D content in terms of both geometry consistency and texture fidelity."
"Our framework can be further applied to more downstream tasks, including user-guided generation and 3D animation."

핵심 통찰 요약

DreamControl

by Tianyu Huang... 게시일 arxiv.org 03-13-2024

https://arxiv.org/pdf/2312.06439.pdf

더 깊은 질문

How does DreamControl address the limitations of existing text-to-3D generation methods

DreamControl addresses the limitations of existing text-to-3D generation methods by introducing a two-stage framework that optimizes a coarse NeRF scene as a 3D self-prior. This approach helps in maintaining consistent geometry and texture fidelity in the generated 3D content. By optimizing the NeRF representation before overfitting, DreamControl can generate high-quality 3D content with improved consistency and detail compared to other methods. The adaptive viewpoint sampling and boundary integrity metric further enhance the generation process, ensuring that the generated priors align with the desired distribution of viewpoints from 2D diffusion models.

What potential ethical considerations should be taken into account when using automated text-to-3D generation tools like DreamControl

When using automated text-to-3D generation tools like DreamControl, several ethical considerations should be taken into account. One major concern is related to potential misuse of generated content for malicious purposes such as creating deepfakes or misleading visual information. Ensuring transparency about the origin of generated content and implementing safeguards against unethical uses are essential steps to mitigate these risks. Additionally, issues related to data privacy and consent must be addressed when utilizing user-provided text prompts for generating 3D content.

How can the concept of self-prior optimization be applied to other domains beyond text-to-3D generation

The concept of self-prior optimization introduced in DreamControl can be applied to other domains beyond text-to-3D generation. For example:

Image Generation: Self-prior optimization could improve image synthesis tasks by leveraging pre-trained representations as priors for generating high-fidelity images.

Video Synthesis: In video synthesis applications, self-priors derived from optimized frames could help maintain consistency across different frames while preserving details during video generation processes.

Medical Imaging: Applying self-prior optimization in medical imaging tasks could assist in enhancing diagnostic accuracy by utilizing optimized prior knowledge for generating detailed medical images or reconstructions.

By adapting the concept of self-prior optimization to various domains, it is possible to improve generative models' performance and output quality across different applications beyond text-to-3D generation tasks.