SeMo3D: Unifying 3D Generation, Editing, and Completion via Selective Modeling

Xiaokun Sun, Jian Yang, Zhenyu Zhang

3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-specific constraints, even when only a local region needs modification. This leads to task isolation, inconsistency in content that should remain unchanged, and dependence on task-specific paired data. We address these issues with a key observation: the three tasks all synthesize a designated region while keeping the rest intact---the full region for generation, the target region for editing, and the missing region for completion. We term this paradigm Selective Modeling and build SeMo3D upon it; it naturally unifies the three tasks, preserves unchanged content by construction, and enables training without paired editing or completion data, since any 3D asset can be partitioned into a selected region and its context. For training, we propose region-selective denoising, which randomly selects regions of varying sizes on standard 3D assets and applies noise and loss only to them, jointly learning all three tasks. For architecture, we propose Omni3DiT, a multi-condition 3D diffusion transformer that distinguishes regions to create from those to retain and fuses image, text,…