MuSteerNet: Human Reaction Generation from Videos via Observation-Reaction Mutual Steering

2026-03-20

AI Summary

Existing methods struggle to generate 3D human reactive motions that are semantically consistent with input videos, often resulting in mismatches between motion and visual content. To address this issue, this work proposes MuSteerNet, a novel framework that introduces an observation–reaction mutual guidance mechanism to alleviate association distortions through prototype-based feedback. The approach further incorporates a dual-coupled reaction refinement module, which integrates a gated incremental correction modulator, relational boundary constraints, and prototype vector learning to enhance motion fidelity. Extensive experiments demonstrate that MuSteerNet significantly outperforms state-of-the-art methods across multiple evaluation metrics, effectively improving the semantic alignment between synthesized reactive motions and the corresponding video context.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Video-driven human reaction generation aims to synthesize 3D human motions that directly react to observed video sequences, which is crucial for building human-like interactive AI systems. However, existing methods often fail to effectively leverage video inputs to steer human reaction synthesis, resulting in reaction motions that are mismatched with the content of video sequences. We reveal that this limitation arises from a severe relational distortion between visual observations and reaction types. In light of this, we propose MuSteerNet, a simple yet effective framework that generates 3D human reactions from videos via observation-reaction mutual steering. Specifically, we first propose a Prototype Feedback Steering mechanism to mitigate relational distortion by refining visual observations with a gated delta-rectification modulator and a relational margin constraint, guided by prototypical vectors learned from human reactions. We then introduce Dual-Coupled Reaction Refinement that fully leverages rectified visual cues to further steer the refinement of generated reaction motions, thereby effectively improving reaction quality and enabling MuSteerNet to achieve competitive performance. Extensive experiments and ablation studies validate the effectiveness of our method. Code coming soon: https://github.com/zhouyuan888888/MuSteerNet.

Problem

• human reaction generation
• video-driven motion synthesis
• relational distortion
• 3D human motion
• observation-reaction alignment

Innovation

• MuSteerNet
• observation-reaction mutual steering
• Prototype Feedback Steering
• relational distortion mitigation
• video-driven human motion generation