AI RESEARCH
DreamCS: Geometry-Aware Text-to-3D Generation with Unpaired 3D Reward Supervision
arXiv CS.CV
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ArXi:2506.09814v3 Announce Type: replace While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation -- leading to geometric artifacts due to their inherent 2D bias.