Abstract
Generating human motion with precise spatial control is a challenging problem. Existing approaches often require task-specific training or slow optimization, and enforcing hard constraints frequently disrupts motion naturalness. Building on the observation that many animation tasks can be formulated as a linear inverse problem, we introduce ProjFlow, a training-free sampler that achieves zero-shot, exact satisfaction of linear spatial constraints while preserving motion realism. Our key advance is a novel kinematics-aware metric that encodes skeletal topology. This metric allows the sampler to enforce hard constraints by distributing corrections coherently across the entire skeleton, avoiding the unnatural artifacts of naive projection. Furthermore, for sparse inputs, such as filling in long gaps between a few keyframes, we introduce a time-varying formulation using pseudo-observations that fade during sampling. Extensive experiments on representative applications, motion inpainting, and 2D-to-3D lifting, demonstrate that ProjFlow achieves exact constraint satisfaction and matches or improves realism over zero-shot baselines, while remaining competitive with training-based controllers.
Abstract (translated)
生成具有精确空间控制的人体运动是一个挑战性问题。现有方法通常需要特定任务的训练或缓慢优化，而强加硬约束常常会破坏动作的自然流畅性。基于观察到许多动画任务可以被表述为线性逆向问题，我们引入了ProjFlow，这是一种无需训练的采样器，能够在不牺牲运动真实感的情况下实现对线性空间约束的零样本、精确满足。我们的关键进展是一种新颖的动力学感知度量方法，该方法编码骨骼拓扑结构。这种度量使得采样器能够通过在整个骨架上合理地分布修正来强制执行硬约束，从而避免了简单投影所产生的不自然效果。此外，在处理稀疏输入（如填充几个关键帧之间的长时间间隙）时，我们提出了一种使用伪观察的随时间变化的形式化方法，该方法在采样过程中逐渐淡化。 通过具代表性的应用场景、运动插补和2D到3D提升进行的广泛实验表明，ProjFlow能够精确满足约束条件，并且在不牺牲自然流畅性的情况下，在零样本基准上保持或提高了真实感。此外，ProjFlow仍然与基于训练的控制器具有竞争力。
URL
https://arxiv.org/abs/2602.22742
