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Exploring Personality in Human-Object Interactions
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AI
DOI：10.1007/s11760-025-04594-7.png)
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Abstract
En 中文
Personality is crucial for accurate and realistic communication in animated humans. Temporal features like body movement help express personality traits. Studies control personality by utilizing high-level motion parameters in general actions, but current research lacks focus on object interaction animations. While object interaction is not a social concept, the subject's personality can affect the motion during interaction. This study examines personality expression in various object interaction sequences to identify the differences due to object types, performed actions, and their iterations. We train a neural motion field-based network to author an animation's intended personality during object interaction, utilizing our personality-aware motion augmentations. We validate our approach with a user study to assess the resulting motions' personality, accuracy, and realism. The results suggest that augmentations better differentiate the positive and negative traits, especially for conscientiousness and extraversion, but at the cost of reduced realism and accuracy. In contrast, data-driven manipulations yield realistic and accurate results, but their impact on personality is subtle. However, when we alter multiple OCEAN factors simultaneously, the resulting changes in the motion are more noticeable.
Keywords:
3D Object Interaction
SMPL-X Body Model
OCEAN Personality Model
Neural Motion Reconstruction
Generative Adversarial Networks
Journal
IF:
2.1
Papers:
877
Citations:
4.6K
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