OpenNovelty.org

HUMOF: Human Motion Forecasting in Interactive Social Scenes

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
human motion forecastingscene-awaremulti-person
Abstract:

Complex dynamic scenes present significant challenges for predicting human behavior due to the abundance of interaction information, such as human-human and human-environment interactions. These factors complicate the analysis and understanding of human behavior, thereby increasing the uncertainty in forecasting human motions. Existing motion prediction methods thus struggle in these complex scenarios. In this paper, we propose an effective method for human motion forecasting in dynamic scenes. To achieve a comprehensive representation of interactions, we design a hierarchical interaction feature representation so that high-level features capture the overall context of the interactions, while low-level features focus on fine-grained details. Besides, we propose a coarse-to-fine interaction reasoning module that leverages both spatial and frequency perspectives to efficiently utilize hierarchical features, thereby enhancing the accuracy of motion predictions. Our method achieves state-of-the-art performance across four public datasets. We will release our code upon publication.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a hierarchical interaction feature representation and coarse-to-fine reasoning module for human motion forecasting in dynamic scenes. It resides in the 'Causal and Interpretable Interaction Reasoning' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader pedestrian trajectory prediction landscape. This leaf emphasizes interpretability and causal structures, distinguishing it from the more crowded graph-based and attention-based sibling categories that prioritize purely data-driven social interaction modeling without explicit causal reasoning.

The taxonomy reveals that the paper's immediate neighbors focus on causal intervention strategies and hierarchical reasoning to reduce spurious correlations. Nearby leaves include 'Graph-Based Social Interaction Learning' and 'Attention-Based Social Reasoning', which collectively contain six papers and represent more established approaches to modeling pedestrian interactions. The 'Spatio-Temporal Graph and Dual-Attention Networks' category, with three papers, also explores combined spatial-temporal modeling but without the causal interpretability emphasis. The paper's hierarchical feature design bridges toward scene-aware methods in sibling branches, though it remains anchored in social interaction modeling rather than explicit 3D scene geometry constraints.

Among thirty candidates examined, the hierarchical interaction feature representation (Contribution A) shows one refutable candidate out of ten examined, suggesting some prior work addresses similar multi-level feature abstractions. The coarse-to-fine reasoning module (Contribution B) and the overall forecasting method (Contribution C) each examined ten candidates with zero refutations, indicating these contributions may be more distinctive within the limited search scope. The analysis does not claim exhaustive coverage; it reflects top-K semantic matches and citation expansion, leaving open the possibility of relevant work outside this candidate set.

Given the sparse population of the causal reasoning leaf and the limited search scope, the work appears to occupy a less-explored niche emphasizing hierarchical and interpretable interaction modeling. The single refutation for Contribution A suggests incremental refinement over existing hierarchical approaches, while Contributions B and C show no clear overlap among examined candidates. However, the thirty-candidate scope and the proximity to more crowded graph-based and attention-based categories warrant caution in claiming strong novelty without broader literature coverage.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: human motion forecasting in interactive social scenes. The field encompasses a diverse set of problem settings and methodological approaches organized into several major branches. Pedestrian Trajectory Prediction in Crowded Environments focuses on anticipating individual and group movements in dense public spaces, often employing graph-based and attention mechanisms to model social interactions. Autonomous Driving and Multi-Agent Vehicle Prediction addresses trajectory forecasting for vehicles and traffic participants, emphasizing safety-critical decision-making. Scene-Aware and Human-Environment Interaction incorporates physical context and environmental constraints into motion models, while Human-Robot Interaction and Socially-Aware Navigation targets collaborative settings where robots must predict and respond to human behavior. Human Motion and Pose Forecasting deals with detailed body-level predictions, Interactive Motion Synthesis and Generation explores controllable motion creation, and Specialized Application Domains cover niche scenarios. Representative works such as Social-STGCNN[37] and Agentformer[17] illustrate how spatial-temporal modeling and transformer architectures have become foundational across multiple branches. Within Pedestrian Trajectory Prediction, a particularly active line of work explores Causal and Interpretable Interaction Reasoning, seeking to move beyond purely data-driven correlations toward understanding the underlying mechanisms of social influence. HUMOF[0] situates itself in this branch, emphasizing causal intervention strategies to disentangle spurious dependencies from genuine social effects. This contrasts with neighboring approaches like Causal Intervention for Human[42], which also targets causal structures but may differ in intervention design or scope, and SocialMP[50], which integrates motion planning with social reasoning but does not necessarily prioritize causal interpretability. The central tension across these works involves balancing model expressiveness with interpretability: while many studies achieve strong predictive performance through complex neural architectures, the causal reasoning branch prioritizes transparency and robustness to distribution shifts, raising open questions about how to scale interpretable methods without sacrificing accuracy in highly dynamic, multi-agent scenarios.

Claimed Contributions

Hierarchical interaction feature representation for human-human and human-scene interactions

The authors design a hierarchical representation that captures interactions at multiple levels: high-level features encode overall semantic context while low-level features capture fine-grained geometric details. This representation encompasses both human-human interactions (through self-encoding and relation-encoding) and human-scene interactions (through multi-level point cloud abstraction).

10 retrieved papers
Can Refute
Coarse-to-fine interaction reasoning module

The authors introduce a reasoning module that processes hierarchical interaction features progressively: high-level features are injected into early Transformer layers for semantic understanding, while low-level features are introduced in later layers for geometric details. This is complemented by a DCT rescaling mechanism that suppresses high-frequency components early and progressively focuses on low-frequency details.

10 retrieved papers
Method for human motion forecasting in dynamic interactive scenes

The authors propose HUMOF, a comprehensive framework that predicts human motion by modeling both human-human and human-scene interactions in complex dynamic environments. The method combines hierarchical interaction representations with coarse-to-fine reasoning to achieve improved prediction accuracy across multiple datasets.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Hierarchical interaction feature representation for human-human and human-scene interactions

The authors design a hierarchical representation that captures interactions at multiple levels: high-level features encode overall semantic context while low-level features capture fine-grained geometric details. This representation encompasses both human-human interactions (through self-encoding and relation-encoding) and human-scene interactions (through multi-level point cloud abstraction).

Contribution

Coarse-to-fine interaction reasoning module

The authors introduce a reasoning module that processes hierarchical interaction features progressively: high-level features are injected into early Transformer layers for semantic understanding, while low-level features are introduced in later layers for geometric details. This is complemented by a DCT rescaling mechanism that suppresses high-frequency components early and progressively focuses on low-frequency details.

Contribution

Method for human motion forecasting in dynamic interactive scenes

The authors propose HUMOF, a comprehensive framework that predicts human motion by modeling both human-human and human-scene interactions in complex dynamic environments. The method combines hierarchical interaction representations with coarse-to-fine reasoning to achieve improved prediction accuracy across multiple datasets.

Table of Contents