Persistent Story World Simulation with Continuous Character Customization

• URL: http://arxiv.org/abs/2603.16285v1
• Date: Tue, 17 Mar 2026 09:17:10 GMT
• Title: Persistent Story World Simulation with Continuous Character Customization
• Abstract summary: We present EverTale, a story world simulator for continuous story character customization.<n>Our EverTale achieves superior performance against a wider range of compared methods on both single- and multi-character story visualization.
• Score: 85.65195925369268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Story visualization has gained increasing attention in computer vision. However, current methods often fail to achieve a synergy between accurate character customization, semantic alignment, and continuous integration of new identities. To tackle this challenge, in this paper we present EverTale, a story world simulator for continuous story character customization. We first propose an All-in-One-World Character Integrator to achieve continuous character adaptation within unified LoRA module, eliminating the need for per-character optimization modules of previous methods. Then, we incorporate a Character Quality Gate via MLLM-as-Judge to ensure the fidelity of each character adaptation process through chain-of-thought reasoning, determining whether the model can proceed to the next character or require additional training on the current one. We also introduce a Character-Aware Region-Focus Sampling strategy to address the identity degradation and layout conflicts in existing multi-character visual storytelling, ensuring natural multi-character generation by harmonizing local character-specific details with global scene context with higher efficiency. Experimental results show that our EverTale achieves superior performance against a wider range of compared methods on both single- and multi-character story visualization. Codes will be available.

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