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arXiv · 2603.16285

EvoTale: Continual Character Customization for Expanding Story Worlds

Jinlu Zhang·Qiyun Wang·Baoxiang Du·Jiayi Ji·Jing He·Rongsheng Zhang·Tangjie Lv·Xiaoshuai Sun·Rongrong Ji
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Abstract

Character-centric story visualization aims to synthesize coherent image sequences that depict narrative events and interactions while preserving recurring character identities. In expanding story worlds, new user-specified characters must be continually incorporated despite varying customization difficulty and identity conflicts in multi-character scenes, without disrupting previously learned identities. In this paper, we propose EvoTale, a continual character customization framework for expanding stylized story worlds. We first introduce an All-in-One-World Character Integrator, which accumulates character-specific residual components within a unified LoRA branch using sparsely overlapping subspaces spanned by a shared orthonormal basis, while freezing previously learned components to limit cross-character coupling. We then develop a Character Quality Gate that uses rubric-guided MLLM feedback as a bounded controller to adapt the optimization budget based on the assessed customization quality. Finally, we propose Character-Aware Region-Focus Sampling, which combines bounding-box-guided regional denoising with identity-aware global denoising to preserve character identities within their designated regions while maintaining global narrative coherence. Experimental results show that EvoTale achieves a favorable balance across character fidelity, continual identity retention, multi-character generation quality, and story-text alignment compared with representative story visualization and customization methods.

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Jinlu Zhang, Qiyun Wang, Baoxiang Du, Jiayi Ji, Jing He, Rongsheng Zhang, Tangjie Lv, Xiaoshuai Sun, Rongrong Ji. 2026-03-17. EvoTale: Continual Character Customization for Expanding Story Worlds. https://arxiv.org/abs/2603.16285

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