MultiCrafter: High-Fidelity Multi-Subject Generation via Spatially
  Disentangled Attention and Identity-Aware Reinforcement Learning
Multi-subject image generation aims to synthesize user-provided subjects in a
single image while preserving subject fidelity, ensuring prompt consistency,
and aligning with human aesthetic preferences. However, existing methods,
particularly those built on the In-Context-Learning paradigm, are limited by
their reliance on simple reconstruction-based objectives, leading to both
severe attribute leakage that compromises subject fidelity and failing to align
with nuanced human preferences. To address this, we propose MultiCrafter, a
framework that ensures high-fidelity, preference-aligned generation. First, we
find that the root cause of attribute leakage is a significant entanglement of
attention between different subjects during the generation process. Therefore,
we introduce explicit positional supervision to explicitly separate attention
regions for each subject, effectively mitigating attribute leakage. To enable
the model to accurately plan the attention region of different subjects in
diverse scenarios, we employ a Mixture-of-Experts architecture to enhance the
model's capacity, allowing different experts to focus on different scenarios.
Finally, we design a novel online reinforcement learning framework to align the
model with human preferences, featuring a scoring mechanism to accurately
assess multi-subject fidelity and a more stable training strategy tailored for
the MoE architecture. Experiments validate that our framework significantly
improves subject fidelity while aligning with human preferences better.
68db399cd2bf1f4b15ec7297
Tao Wu
claimed_verified
2025-09-30T06:34:46.687Z
676a2ca72d7050defde9b25d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/qNWwTpxhHfbRSHdtdDhQl.png
Suger
user
SugerWu
68db399cd2bf1f4b15ec7298
Yibo Jiang
68db399cd2bf1f4b15ec7299
Yehao Lu
68db399cd2bf1f4b15ec729a
Zhizhong Wang
68db399cd2bf1f4b15ec729b
Zeyi Huang
68db399cd2bf1f4b15ec729c
Zequn Qin
68db399cd2bf1f4b15ec729d
Xi Li
MultiCrafter: High-Fidelity Multi-Subject Generation via Spatially
  Disentangled Attention and Identity-Aware Reinforcement Learning
Multi-subject image generation aims to synthesize user-provided subjects in a
single image while preserving subject fidelity, ensuring prompt consistency,
and aligning with human aesthetic preferences. However, existing methods,
particularly those built on the In-Context-Learning paradigm, are limited by
their reliance on simple reconstruction-based objectives, leading to both
severe attribute leakage that compromises subject fidelity and failing to align
with nuanced human preferences. To address this, we propose MultiCrafter, a
framework that ensures high-fidelity, preference-aligned generation. First, we
find that the root cause of attribute leakage is a significant entanglement of
attention between different subjects during the generation process. Therefore,
we introduce explicit positional supervision to explicitly separate attention
regions for each subject, effectively mitigating attribute leakage. To enable
the model to accurately plan the attention region of different subjects in
diverse scenarios, we employ a Mixture-of-Experts architecture to enhance the
model's capacity, allowing different experts to focus on different scenarios.
Finally, we design a novel online reinforcement learning framework to align the
model with human preferences, featuring a scoring mechanism to accurately
assess multi-subject fidelity and a more stable training strategy tailored for
the MoE architecture. Experiments validate that our framework significantly
improves subject fidelity while aligning with human preferences better.
MultiCrafter framework improves multi-subject image generation by addressing attribute leakage through explicit positional supervision, utilizing a Mixture-of-Experts architecture, and aligning with human preferences via online reinforcement learning.
multi-subject image generation attribute leakage explicit positional supervision Mixture-of-Experts architecture online reinforcement learning scoring mechanism