3SGen: Unified Subject, Style, and Structure-Driven Image Generation with Adaptive Task-specific Memory

• URL: http://arxiv.org/abs/2512.19271v1
• Date: Mon, 22 Dec 2025 11:07:27 GMT
• Title: 3SGen: Unified Subject, Style, and Structure-Driven Image Generation with Adaptive Task-specific Memory
• Authors: Xinyang Song, Libin Wang, Weining Wang, Zhiwei Li, Jianxin Sun, Dandan Zheng, Jingdong Chen, Qi Li, Zhenan Sun,
• Abstract summary: 3SGen is a task-aware unified framework that performs all three conditioning modes within a single model.<n>At its core, an Adaptive Task-specific Memory (ATM) module dynamically disentangles, stores, and retrieves condition-specific priors.<n>We propose 3SGen-Bench, a unified image-driven generation benchmark with standardized metrics for evaluating cross-task fidelity and controllability.
• Score: 54.056509629389915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent image generation approaches often address subject, style, and structure-driven conditioning in isolation, leading to feature entanglement and limited task transferability. In this paper, we introduce 3SGen, a task-aware unified framework that performs all three conditioning modes within a single model. 3SGen employs an MLLM equipped with learnable semantic queries to align text-image semantics, complemented by a VAE branch that preserves fine-grained visual details. At its core, an Adaptive Task-specific Memory (ATM) module dynamically disentangles, stores, and retrieves condition-specific priors, such as identity for subjects, textures for styles, and spatial layouts for structures, via a lightweight gating mechanism along with several scalable memory items. This design mitigates inter-task interference and naturally scales to compositional inputs. In addition, we propose 3SGen-Bench, a unified image-driven generation benchmark with standardized metrics for evaluating cross-task fidelity and controllability. Extensive experiments on our proposed 3SGen-Bench and other public benchmarks demonstrate our superior performance across diverse image-driven generation tasks.

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