---
title: "Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25984v1"
permalink: "https://feed.astralor.com/items/itm_09cad5bf938569cb009b078e"
published: "2026-07-28T17:05:39.000Z"
score: 75
channel: "ai"
tags: ["GARFIELD","Uncertainty-Aware Planning","Scene Kinematics","Probabilistic Modeling"]
---

# Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics

## AI commentary

> This paper is noteworthy because it pushes probabilistic modeling to new heights—not only predicting future possibilities but also quantifying uncertainty per element and enabling real-time interaction. It is particularly important for researchers in robotic manipulation or autonomous driving planning; GARFIELD's sampling speed allows it to be embedded in closed-loop systems for real-time decision-making. However, caution is needed as it has only been tested on constrained datasets; generalization to the real world remains a gap.

## At a glance

The paper introduces GARFIELD, a probabilistic model that learns a structured spatio-temporal latent distribution over possible scene kinematics from an image and sparse constraints. It enables joint trajectory sampling and direct density estimation via a deterministic decoder, localizing uncertainty to specific scene elements and timesteps. Compared to large video generation models, it samples trajectories 97× faster and estimates motion densities two orders of magnitude faster, enabling interactive exploration and uncertainty-aware planning.

## Key points

- GARFIELD models scene kinematics probabilistically via a structured latent distribution, enabling joint sampling of multiple trajectories.
- A deterministic density decoder directly estimates motion densities, achieving two orders of magnitude speedup over Monte Carlo sampling.
- Uncertainty localizes to specific scene elements and timesteps, and can be refined with additional constraints.
- Achieves competitive motion planning performance with large video generation models while sampling trajectories 97× faster.

## Full text

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.

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_09cad5bf938569cb009b078e