Bootstrap3D is a framework aimed at improving 3D content creation by leveraging synthetic data generation techniques to address the scarcity of high-quality 3D assets. It utilizes 2D and video diffusion models to generate multi-view images based on text prompts and employs the 3D-perceptive MV-LLaVA model to filter high-quality data and rewrite inaccurate titles. The framework has generated 1 million high-quality synthetic multi-view images with dense descriptive captions to alleviate the shortage of high-quality 3D data. It also introduces a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while preserving the original 2D diffusion prior.