Text-driven in-game 3D character auto-customization systems eliminate the complicated process of manipulating intricate character control parameters. However, current methods are limited by their single-round generation, incapable of further editing and fine-grained modification. In this paper, we propose an Interactive Character Editing framework (ICE) to achieve a multi-round dialogue-based refinement process. In a nutshell, our ICE offers a more user-friendly way to enable players to convey creative ideas iteratively while ensuring that created characters align with the expectations of players. Specifically, we propose an Instruction Parsing Module (IPM) that utilizes large language models (LLMs) to parse multi-round dialogues into clear editing instruction prompts in each round. To reliably and swiftly modify character control parameters at a fine-grained level, we propose a Semantic-guided Low-dimension Parameter Solver (SLPS) that edits character control parameters according to prompts in a zero-shot manner. Our SLPS first localizes the character control parameters related to the fine-grained modification, and then optimizes the corresponding parameters in a low-dimension space to avoid unrealistic results. Extensive experimental results demonstrate the effectiveness of our proposed ICE for in-game character creation and the superior editing performance of ICE. Project page: https://iceedit.github.io/.

翻译：文本驱动的游戏内三维角色自动定制系统简化了对复杂角色控制参数的操作过程。然而，当前方法受限于单轮生成，无法进行进一步编辑和细粒度修改。本文提出了一种交互式角色编辑框架（ICE），以实现基于多轮对话的迭代优化过程。简而言之，我们的ICE提供了一种更用户友好的方式，使玩家能够迭代地传达创意想法，同时确保创建的角色符合玩家的期望。具体来说，我们提出了一种指令解析模块（IPM），该模块利用大语言模型（LLMs）将多轮对话解析为每轮清晰的编辑指令提示。为了可靠且快速地以细粒度级别修改角色控制参数，我们提出了一种语义引导的低维参数求解器（SLPS），该求解器以零样本方式根据提示编辑角色控制参数。我们的SLPS首先定位与细粒度修改相关的角色控制参数，然后在低维空间中优化相应参数，以避免不真实的结果。大量实验结果表明，我们提出的ICE在游戏内角色创建中的有效性以及ICE的卓越编辑性能。项目页面：https://iceedit.github.io/。