Who is a Better Dreamer: Subjective and Objective Quality Assessment for Text-to-3D Generated Contents

Text-to-3D content generation, often referred as “Dreamer," has emerged as a rapid solution for 3D modeling, yet current systems still suffer from pronounced quality deficiencies that undermine user experience. To systematically evaluate the quality of 3D generated contents (3DGCs) and to inform the future development of Dreamers, we present a comprehensive subjective and objective assessment. We benchmark 11 representative Dreamers using 484 prompts and collect 4,691 3DGCs, forming 3DGCQA-LS, the largest 3DGC quality assessment dataset to date. Through controlled multi-subject evaluation, we uncover substantial performance discrepancies across existing Dreamers and identify nine recurring distortion types. To further enable automated quality prediction, we introduce Hyper-VG, a novel 3DGC quality assessment framework that integrates the Huanyuan world model, persistent homotopy theory, and cube projection. Extensive experiments demonstrate that Hyper-VG achieves state-of-the-art (SOTA) performance across multiple benchmark datasets, providing a strong foundation for advancing reliable Dreamers. This work will be made open source to advance the field.