AI RESEARCH
ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation
arXiv CS.CV
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ArXi:2504.02316v4 Announce Type: replace Recent advances in zero-shot text-to-3D generation have revolutionized 3D content creation by enabling direct synthesis from textual descriptions. While state-of-the-art methods leverage 3D Gaussian Splatting with score distillation to enhance multi-view rendering through pre-trained text-to-image (T2I) models, they suffer from inherent prior view biases in T2I priors. These biases lead to inconsistent 3D generation, particularly manifesting as the multi-face Janus problem, where objects exhibit conflicting features across views.