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Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model
April 30, 2024, 4:46 a.m. | Xiaolong Li, Jiawei Mo, Ying Wang, Chethan Parameshwara, Xiaohan Fei, Ashwin Swaminathan, CJ Taylor, Zhuowen Tu, Paolo Favaro, Stefano Soatto
cs.CV updates on arXiv.org arxiv.org
Abstract: In this paper, we propose an effective two-stage approach named Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts while achieving high fidelity by using a pre-trained multi-view diffusion model. Multi-view diffusion models, such as MVDream, have shown to generate high-fidelity 3D assets using score distillation sampling (SDS). However, applied naively, these methods often fail to comprehend compositional text prompts, and may often entirely omit certain subjects or parts. To address …
abstract arxiv cs.ai cs.cv diffusion diffusion model diffusion models diverse fidelity generate paper prompts stage text type view while
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