让大模型生成的运动轨迹自动对齐，提升准确率与可信度。
Self-Consistency for LLM-Based Motion Trajectory Generation and Verification
- 用几何变换建模轨迹家族，通过聚类识别一致路径。
- 轨迹生成准确率提升4%-6%，验证精度比视觉模型高11%。
- 适合做轨迹生成、验证的AI研究者和开发者使用。
自一致性已被证明是提升大模型在自然语言推理任务中性能的有效方法，且无需额外标注。本文研究如何将自一致性拓展至视觉领域，聚焦于大模型生成的运动轨迹的生成与验证。给定一个提示（如“让圆沿螺旋路径移动”），我们首先从大模型中采样多种运动轨迹，再通过聚类识别出一致的轨迹组。核心思想是将提示对应的轨迹形状家族建模为一个原型轨迹及其一组几何变换（如刚性、相似性、仿射变换）。若两个轨迹可通过允许的变换相互映射，则视为一致。我们提出一种算法，自动推断形状家族，利用候选变换组间的层次关系。该方法使大模型轨迹生成准确率提升4%-6%。进一步扩展至验证任务，相较视觉语言模型基线实现11%的精度提升。代码与数据集已公开于 https://majiaju.io/trajectory-self-consistency。
原文摘要 · Abstract (English)
Self-consistency has proven to be an effective technique for improving LLM performance on natural language reasoning tasks in a lightweight, unsupervised manner. In this work, we study how to adapt self-consistency to visual domains. Specifically, we consider the generation and verification of LLM-produced motion graphics trajectories. Given a prompt (e.g., "Move the circle in a spiral path"), we first sample diverse motion trajectories from an LLM, and then identify groups of consistent trajectories via clustering. Our key insight is to model the family of shapes associated with a prompt as a prototype trajectory paired with a group of geometric transformations (e.g., rigid, similarity, and affine). Two trajectories can then be considered consistent if one can be transformed into the other under the warps allowable by the transformation group. We propose an algorithm that automatically recovers a shape family, using hierarchical relationships between a set of candidate transformation groups. Our approach improves the accuracy of LLM-based trajectory generation by 4-6%. We further extend our method to support verification, observing 11% precision gains over VLM baselines. Our code and dataset are available at https://majiaju.io/trajectory-self-consistency .
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