AI RESEARCH ANALYSIS
Incorporating Eye-Tracking Signals into Multimodal Deep Visual Models for Predicting User Aesthetic Experience in Residential Interiors
This study introduces a novel dual-branch CNN-LSTM framework that integrates visual features from interior design videos with eye-tracking signals to predict user aesthetic evaluations of residential interiors. The model achieves high accuracy on both objective and subjective aesthetic dimensions, outperforming state-of-the-art video baselines. A key finding is that eye-tracking data serves as 'privileged information' during training, enhancing model performance without being required during inference, making the tool practical for real-world design applications. The model's attention mechanisms align with human perception, demonstrating how visual and gaze cues contribute to aesthetic assessment.
Executive Impact & Key Metrics
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Objective Accuracy
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Subjective Accuracy
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Pupil Dynamics Contribution
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Time Saved in Evaluation
Deep Analysis & Enterprise Applications
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Focus: Human Building Interaction
This category explores the complex interplay between human perception, behavior, and the built environment. AI models in this domain aim to understand and predict human responses to design, optimize comfort, and enhance well-being within architectural spaces.
72.2%
Accuracy on Objective Aesthetic Dimensions (e.g., Light)
Enterprise Process Flow
Video Frames
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Pupil Response Images
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Spatial Gaze-Guided Attention
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Temporal Frame Attention
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Multimodal Fusion
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Aesthetic Prediction
| Method | Objective Accuracy | Subjective Accuracy |
|---|---|---|
| I3D | 57.2% | 51.6% |
| X3D | 62.9% | 52.5% |
| Video Swin | 72.1% | 64.9% |
| TimeSformer | 74.3% | 60.0% |
| VideoMamba | 61.5% | 53.2% |
| Ours (Full Model) | 72.2% | 66.8% |
Real-World Application in Interior Design
Our model, trained with eye-tracking as privileged information, can assess aesthetic evaluations from video alone with 72.8% objective accuracy and 67.0% subjective accuracy. This enables practical tools for designers to receive data-driven feedback on various aesthetic dimensions, such as comfort, hominess, and uplift, significantly improving design iteration cycles and user satisfaction without requiring specialized equipment for gaze tracking during deployment.
Key Benefits:
- Data-driven design feedback
- Improved design iteration
- Enhanced user satisfaction
- Reduced need for manual surveys
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Implementation Roadmap
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Phase 1: Discovery & Strategy
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Phase 2: Data Integration & Model Adaptation
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Phase 3: Pilot Deployment & Feedback
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Phase 4: Full-Scale Rollout & Optimization
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