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Abstract <p>Forecasting the future state of a scene is a key computer vision task needed to build systems capable of proactive perception and decision-making in changing environments. This work addresses the problem of forecasting future scene graphs, where, given a video and a sequence of past graphs, one must predict objects and their relations in subsequent frames. Unlike existing approaches limited to static perception, the proposed method, GraphCast, takes into account semantic vision-language features of objects and their temporal dynamics. We introduce a model architecture based on object-centric encoding with a foundation transformer model, interaction modeling via a biaffine relation classification head, and a specialized object presence classifier. In addition, a temporal convolution module is used to extract features and improve robustness to noise. Experiments on the STAR and Action Genome datasets demonstrate that the proposed architecture outperforms existing baselines.</p>

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Scene Graph Forecasting Using Neural Network-Based Methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • A. M. Trunova,
  • D. A. Yudin

摘要

Abstract

Forecasting the future state of a scene is a key computer vision task needed to build systems capable of proactive perception and decision-making in changing environments. This work addresses the problem of forecasting future scene graphs, where, given a video and a sequence of past graphs, one must predict objects and their relations in subsequent frames. Unlike existing approaches limited to static perception, the proposed method, GraphCast, takes into account semantic vision-language features of objects and their temporal dynamics. We introduce a model architecture based on object-centric encoding with a foundation transformer model, interaction modeling via a biaffine relation classification head, and a specialized object presence classifier. In addition, a temporal convolution module is used to extract features and improve robustness to noise. Experiments on the STAR and Action Genome datasets demonstrate that the proposed architecture outperforms existing baselines.