CoPlace: Joint Subject Placement and Empty Scene Planning for Sequential Image Generation

Multi-modal Procedure Planning (MPP) generates textual and visual steps towards accomplishing a specific goal. While recent developments in large language models have empowered effective textual reasoning and planning, generating coherent visual procedures remains challenging due to the requirements of subject consistency, state transition coherence, and subject-background harmony. Existing methods typically treat these problems separately, leading to inevitable trade-offs. To address these challenges, we propose Transformation, Plan then Place (TPP), a novel multi-stage framework for sequential image generation. TPP decomposes the generation process into three stages: subject state transformation, joint subject layout and empty-scene planning, and subject placement. We also introduce CoPlace, a joint generation model, where layout planning and scene generation iteratively constrain each other during the denoising process. Furthermore, we propose a space-gating mechanism that suppresses text leakage within subject placement regions, encouraging the generation of a semantically empty subject placeholder that is compatible with global scene context. Extensive experiments on HD-EPIC, LayoutSAM, and HiCo-7K demonstrate that our methods achieve superior performance in improving layout planning, image quality, and geometric compatibility between subjects and backgrounds. Our approach provides a flexible solution for generating coherent multi-step visual procedures while effectively balancing subject fidelity, text alignment and scene controllability.