BoostDream refines text-to-3D generation for high-quality assets

Researchers have developed BoostDream, a novel method for refining text-to-3D generation to produce high-quality assets efficiently. This approach combines fast feed-forward generation with a multi-view Score Distillation Sampling (SDS) loss, overcoming the slower pace of traditional SDS methods. BoostDream also addresses the Janus problem, where generated 3D models have inconsistencies when viewed from different angles, by using prompt and multi-view consistent normal maps as guidance. AI

影响 This method could significantly improve the speed and quality of 3D asset creation for applications like gaming and virtual reality.

排序理由 This is a research paper detailing a new method for text-to-3D generation. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• arXiv
• BoostDream
• Janus problem
• Score Distillation Sampling

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