MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D
  Content Creation
Authors: name: Sankalp Sinha, name: Mohammad Sadil Khan, name: Muhammad Usama, name: Shino Sam, name: Didier Stricker, name: Sk Aziz Ali, name: Muhammad Zeshan Afzal
Organization: Augmented Vision

Generating high-fidelity 3D content from text prompts remains a significant
challenge in computer vision due to the limited size, diversity, and annotation
depth of the existing datasets. To address this, we introduce MARVEL-40M+, an
extensive dataset with 40 million text annotations for over 8.9 million 3D
assets aggregated from seven major 3D datasets. Our contribution is a novel
multi-stage annotation pipeline that integrates open-source pretrained
multi-view VLMs and LLMs to automatically produce multi-level descriptions,
ranging from detailed (150-200 words) to concise semantic tags (10-20 words).
This structure supports both fine-grained 3D reconstruction and rapid
prototyping. Furthermore, we incorporate human metadata from source datasets
into our annotation pipeline to add domain-specific information in our
annotation and reduce VLM hallucinations. Additionally, we develop MARVEL-FX3D,
a two-stage text-to-3D pipeline. We fine-tune Stable Diffusion with our
annotations and use a pretrained image-to-3D network to generate 3D textured
meshes within 15s. Extensive evaluations show that MARVEL-40M+ significantly
outperforms existing datasets in annotation quality and linguistic diversity,
achieving win rates of 72.41% by GPT-4 and 73.40% by human evaluators.