Part-aware 3D generation works better when you explicitly model semantic relationships between parts derived from language, not just their geometry—this enables text descriptions to guide both individual part structure and how parts fit together.
DreamPartGen generates 3D objects from text by understanding them as meaningful parts with semantic relationships. Unlike existing methods that focus only on geometry, this approach jointly models each part's shape and appearance while capturing how parts relate to each other based on the text description, resulting in more coherent and interpretable 3D models.