Sparse anchors provide a compact interface for human motion authoring: users specify a few root positions, planar trajectory samples, or body-point targets, while the system synthesizes the full-body motion that completes the under-specified intent. We present AnchorRoute, a sparse-anchor motion synthesis framework that uses anchors as a shared scaffold for both generation and refinement. Before generation, AnchorRoute converts sparse anchors into anchor-condition features and injects the resulting condition memory into a frozen Transition Masked Diffusion prior through AnchorKV and dual-context conditioning. This preserves the generation quality of the pretrained text-to-motion prior while learning sparse spatial control. After generation, the same anchors are evaluated as residuals: their timestamps define refinement intervals, and their residuals determine where correction should be concentrated. RouteSolver then refines the motion by projecting soft-token updates onto anchor-defined piecewise-affine interval bases. This couples generation-time anchor conditioning with residual-routed refinement under one anchor scaffold. AnchorRoute supports root-3D, planar-root, and body-point control within the same formulation. In benchmark evaluations, AnchorRoute outperforms prior sparse-control methods under the sparse keyjoint protocol and consistently improves anchor adherence across control families. The results show that the learned anchor-conditioned generator and RouteSolver refinement are complementary: the generator preserves text-motion quality, while RouteSolver provides a controllable path toward stronger anchor adherence.

翻译：稀疏锚点提供了一种紧凑的人体动作创作接口：用户指定少量根位置、平面轨迹采样点或身体关键点目标，系统则合成完整的全身动作以补全未充分指定的意图。我们提出AnchorRoute——一种基于稀疏锚点的动作合成框架，利用锚点作为生成与优化环节的共享支架。在生成阶段，AnchorRoute将稀疏锚点转换为锚点条件特征，并通过AnchorKV和双上下文条件机制将生成的条件记忆注入冻结的Transition Masked Diffusion先验模型。这一过程在保留预训练文本到动作先验模型生成质量的同时，学习稀疏空间控制能力。生成完成后，相同的锚点作为残差被评估：其时间戳定义优化区间，残差值确定需要集中修正的位置。随后RouteSolver通过将软令牌更新投影到锚点定义的分段线性区间基上，实现动作的精细化优化。该设计将生成阶段的锚点条件与残差路由优化统一于锚点支架框架之下。AnchorRoute支持根3D轨迹、平面轨迹和身体关键点控制，且所有控制均采用统一公式。在基准测试中，AnchorRoute在稀疏关键点协议下优于先前的稀疏控制方法，并在多种控制类型上持续提升锚点贴合度。结果表明，经学习的锚点条件生成器与RouteSolver优化具有互补性：生成器保持文本-动作质量，而RouteSolver为增强锚点贴合度提供了可控路径。