想快速预测场景多种运动轨迹？GARFIELD比大模型快97倍，还能实时估算不确定性。
GARFIELD是一种概率场景运动模型，能从单张图像和稀疏时空约束中学习未来运动分布的潜在表示。它通过确定性密度解码器直接访问运动分布，实现轨迹联合采样和密度估计。实验表明，在运动规划任务上性能与大型视频生成模型相当，但采样速度比后者快97倍。密度估计速度比蒙特卡洛采样快两个数量级，支持交互式规划。
Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics
Predicting how a scene may evolve from partial observations requires reasoning about multiple possible futures rather than committing to a single trajectory. Existing approaches either generate appearance-dominated video predictions or sample a small number of trajectories without explicitly modeling the distribution of possible motion. We introduce Goal-Aware Representations of Future kInEmatic Latent Distributions (GARFIELD), a probabilistic model of scene kinematics that learns a structured spatio-temporal latent representation of the distribution over possible futures given an image and optional spatio-temporally sparse constraints. The same latent representation enables both joint sampling of all trajectories and direct access to the underlying motion distribution through an efficient deterministic density decoder. As a result, uncertainty about future motion can be localized to specific scene elements and timesteps and progressively refined through additional constraints. Experiments demonstrate strong motion planning performance competitive with large video generation models while sampling trajectories $97\times$ faster. Our method further estimates motion densities two orders of magnitude faster than Monte-Carlo sampling from motion generation models, enabling interactive exploration and uncertainty-aware planning.