Partial3D: Controllable 3D Asset Generation from Multiple Partial Images

While 3D generative models have achieved unprecedented visual quality, controlling these systems remains an underexplored frontier compared to 2D generation, primarily due to the significant data bottleneck associated with high-quality 3D assets. Consequently, existing approaches for 3D control often rely on indirect pipelines that leverage 2D control methods and lift the results to 3D, a process that frequently leads to error accumulation and lacks an efficient feed-forward mechanism. In this paper, we propose Partial3D, a native 3D generative model that utilizes partial images, optional 3D layouts, and text for precise and controllable 3D synthesis, enabling native 3D control without relying on intermediate 2D pipelines. Our main insight is to treat partial images as separable visual conditions, allowing for compositional control over the generated 3D content, analogous to how text prompts are used in 2D generation. To address the inherent ambiguity of composing partial images, Partial3D incorporates optional 3D layout guidance through a Spatial Prompt Modulator (SPM) and text guidance through a training-free velocity-fusion strategy. By treating partial images as separable conditioning signals, while incorporating 3D layouts and text prompts, Partial3D enables native 3D editing through image replacement and supports unified control across image, text, and 3D layout modalities. Extensive experiments demonstrate that Partial3D generates 3D content under precise multimodal control and successfully enables downstream applications such as high-fidelity 3D editing.