通过领导-跟随框架实现受轨迹约束的交互式动作生成。
Leader and Follower: Interactive Motion Generation under Trajectory Constraints
- 将复杂动作解耦为领导与跟随者,提升轨迹控制能力。
- 无需重新训练,在多场景下生成更真实、准确的动作。
- 适合需要精确轨迹控制的游戏与影视动作生成应用。
随着游戏和影视制作的快速发展,基于文本生成交互动作受到广泛关注,有望革新内容创作流程。在许多实际应用中,虚拟角色的动作范围或轨迹需严格约束。然而,仅依赖文本输入的方法难以准确捕捉用户意图,尤其在指定目标轨迹时,生成动作常缺乏合理性和准确性。现有基于轨迹的定制化动作生成方法在单角色场景中依赖重训练,限制了对不同数据集的适应性,且难以实现双角色间的交互。为此,本文受双人舞蹈角色分配启发,将复杂动作解耦为领导-跟随动态,提出一种无需训练的方法,融合节奏控制器(Pace Controller)与运动学同步适配器(Kinematic Synchronization Adapter)。该框架通过控制领导者运动并校正跟随者动作以对齐领导者,增强模型对轨迹信息的利用能力。实验表明,所提方法在真实性和准确性上均优于现有方法。
原文摘要 · Abstract (English)
With the rapid advancement of game and film production, generating interactive motion from texts has garnered significant attention due to its potential to revolutionize content creation processes. In many practical applications, there is a need to impose strict constraints on the motion range or trajectory of virtual characters. However, existing methods that rely solely on textual input face substantial challenges in accurately capturing the user's intent, particularly in specifying the desired trajectory. As a result, the generated motions often lack plausibility and accuracy. Moreover, existing trajectory - based methods for customized motion generation rely on retraining for single - actor scenarios, which limits flexibility and adaptability to different datasets, as well as interactivity in two-actor motions. To generate interactive motion following specified trajectories, this paper decouples complex motion into a Leader - Follower dynamic, inspired by role allocation in partner dancing. Based on this framework, this paper explores the motion range refinement process in interactive motion generation and proposes a training-free approach, integrating a Pace Controller and a Kinematic Synchronization Adapter. The framework enhances the ability of existing models to generate motion that adheres to trajectory by controlling the leader's movement and correcting the follower's motion to align with the leader. Experimental results show that the proposed approach, by better leveraging trajectory information, outperforms existing methods in both realism and accuracy.
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