#text-to-3D

GaussianDreamer facilitates the swift creation of detailed 3D models from text prompts by combining 2D and 3D diffusion models. Implementing advanced 3D Gaussian splatting, it enhances both speed and fidelity, rendering real-time 3D outputs in just 15 minutes using a single GPU. Through techniques like noise addition and color variation, it ensures 3D consistency and clarity. Suitable for animation and simulation integration, it allows for Unity export and avatar generation, providing utility across multiple applications.

Explore a novel method for evaluating text-to-3D generative models using GPT-4V for improved human alignment. This project introduces 110 text-aligned image prompts and facilitates background removal with advanced tools. Key components include clear installation steps, evaluation guidelines for text-to-3D models, and a structured scoring system for competitions. Future developments include visualization tools and a Text-to-3D Leaderboard. Designed for researchers aiming for a comprehensive evaluation approach, these metrics help models excel in the competitive arena.

Discover a text-to-3D generation model utilizing a two-step diffusion process. The first stage swiftly produces 3D candidates with diffusion models, and the second stage refines the chosen assets for enhanced accuracy. This method facilitates efficient, high-quality 3D asset creation. Access the latest updates and technical reports for detailed insights.