通过优化扩散模型实现跨风格动作的流畅衔接，适用于舞蹈编排等场景。
Diffusion Path Alignment for Long-Range Motion Generation and Domain Transitions
- 基于扩散模型的推理时优化框架，显式建模动作过渡轨迹。
- 在不同动作域间生成高保真、时间连贯的长程运动序列。
- 首次提供可控制长距离动作生成的通用方法，适合动作合成与创作。
长程人体运动生成仍是计算机视觉与图形学中的核心挑战。跨语义差异动作域的连贯过渡尚未被充分探索。该能力对舞蹈编排等应用尤为重要，要求动作能流畅跨越多样风格与语义主题。本文提出一种受扩散随机最优控制启发的简单而有效的推理时优化框架，引入控制能量目标，显式正则化预训练扩散模型的过渡轨迹。实验表明，推理时优化该目标可生成具有高保真度和时间一致性的过渡序列。这是首个提供可控长程人体运动生成且具备显式过渡建模能力的通用框架。
原文摘要 · Abstract (English)
Long-range human movement generation remains a central challenge in computer vision and graphics. Generating coherent transitions across semantically distinct motion domains remains largely unexplored. This capability is particularly important for applications such as dance choreography, where movements must fluidly transition across diverse stylistic and semantic motifs. We propose a simple and effective inference-time optimization framework inspired by diffusion-based stochastic optimal control. Specifically, a control-energy objective that explicitly regularizes the transition trajectories of a pretrained diffusion model. We show that optimizing this objective at inference time yields transitions with fidelity and temporal coherence. This is the first work to provide a general framework for controlled long-range human motion generation with explicit transition modeling.
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