New probabilistic model GARFIELD predicts scene evolution with enhanced speed

Researchers have developed GARFIELD (Goal-Aware Representations of Future kInEmatic Latent Distributions), a novel probabilistic model designed to predict the evolution of scenes from partial observations. Unlike existing methods that generate appearance-dominated videos or sample limited trajectories, GARFIELD learns a structured latent representation of the distribution over possible future motions. This allows for efficient joint sampling of all trajectories and direct access to the motion distribution, enabling localized uncertainty refinement and interactive planning. Experiments show GARFIELD achieves motion planning performance competitive with large video generation models while being significantly faster in trajectory sampling and density estimation. AI

IMPACT Enables more efficient and interactive motion planning by accurately modeling scene evolution uncertainty.

RANK_REASON The item is an academic paper detailing a new probabilistic model for scene kinematics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• GARFIELD
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• Schrödinger's cat
• ScienceCast
• scite Smart Citations

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →