当蒸馏破坏运动控制时：恢复快速视频生成器的生成轨迹
When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators
AI总结：
研究免训练运动定制应用于蒸馏视频生成器的问题，提出MotionEcho框架，通过推理时用高质量扩散教师模型校正学生模型采样轨迹，实现准确运动控制，提升运动保真度和视觉质量，且保留蒸馏生成效率优势。
摘要：
免训练运动定制通过测试时计算将参考视频中的运动模式应用于视频生成器。大多数现有方法针对全扩散模型，需要许多去噪步骤和高计算成本。随着高效蒸馏模型的兴起，能否将测试时运动定制直接应用于蒸馏生成器？分析表明现有免训练技术在蒸馏模型上失败。蒸馏改变了去噪动态，蒸馏生成器的大去噪步骤丢弃了得分引导所需的密集中间状态。为此提出MotionEcho，一种新颖的免训练测试时蒸馏框架，通过在推理时用高质量扩散教师模型的有限使用来校正学生模型的采样轨迹。教师通过将学生的端点重新噪声化到其密集轨迹上以形成运动对齐的干净端点，然后与学生的端点进行插值，同时自适应调度机制确定何时以及需要多少教师指导。实验表明该方法在保持蒸馏生成效率优势的同时，显著提高了运动保真度和视觉质量。
英文摘要：
Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion models, requiring many denoising steps and high computational cost. With the rise of efficient distilled models, a natural question arises: can test-time motion customization be applied directly to distilled generators with their accelerated sampling and efficiency gains? However, our analysis reveals that existing training-free techniques fail on distilled models. Distillation fundamentally alters the denoising dynamics that prior test-time guidance relies on, and the large denoising steps of distilled generators discard the dense intermediate states that score guidance requires, rendering existing motion control strategies incompatible with fast generation. To address this limitation, we propose MotionEcho, a novel training-free test-time distillation framework that enables motion customization for distilled video generators. The key idea is to correct the student model's sampling trajectory with restricted usage of a high-quality diffusion teacher at inference time. Teacher supervises the student's denoising by re-noising the student's endpoint onto its dense trajectory to form a motion-aligned clean endpoint, then interpolating it with the student's, while an adaptive scheduling mechanism determines when and how much teacher guidance is needed. As a result, MotionEcho restores generative trajectories for distilled video generators via lightweight, adaptive test-time teacher guidance, enabling accurate motion control without compromising generation efficiency. Extensive experiments on multiple distilled video generation models demonstrate that our method significantly improves motion fidelity and visual quality while retaining the efficiency advantages of distilled generation.
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