ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation
High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typically requires learning geometry from scratch and overlooks the rich semantic and structural priors already encapsulated in discriminative 3D foundation models. We contend that leveraging the profound understanding of the 3D world possessed by these discriminative models can significantly reduce generative cost. To this end, we propose ROAD, a framework that reduces the training cost of 3D generation by transferring these rich discriminative priors into diffusion transformers. To address the inherent semantic-structural heterogeneity between ...
| Authors | Xiao Luo, Mingyang Du, Xin Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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