We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset.

翻译：本文提出MotionDiffuser，这是一种基于扩散的表示方法，用于描述多智能体未来轨迹的联合分布。该表示法具有若干关键优势：首先，模型学习高度多模态的分布，能捕捉多样化的未来结果。其次，简单的预测器设计仅需单一L2损失训练目标，且无需依赖轨迹锚点。第三，模型能以置换不变的方式学习多智能体运动的联合分布。此外，我们通过主成分分析（PCA）利用压缩轨迹表示，提升了模型性能，并允许高效计算精确样本对数概率。随后，我们提出一种通用约束采样框架，能够基于可微分代价函数实现受控轨迹采样。该策略支持诸多应用，如强制执行规则与物理先验，或创建定制化仿真场景。MotionDiffuser可与现有骨干架构结合，达到顶尖的运动预测性能。我们在Waymo开放运动数据集上取得了多智能体运动预测的最新成果。