Personality Perception in Human Videos Altered by Motion Transfer Networks
Not on the map. Why?
Abstract (arXiv)
The successful portrayal of personality in digital characters improves communication and immersion. Current research focuses on expressing personality through modifying animations using heuristic rules or data-driven models. While studies suggest motion style highly influences the apparent personality, the role of appearance can be similarly essential. This work analyzes the influence of movement and appearance on the perceived personality of short videos altered by motion transfer networks. We label the personalities in conference video clips with a user study to determine the samples that best represent the Five-Factor model's high, neutral, and low traits. We alter these videos using the Thin-Plate Spline Motion Model, utilizing the selected samples as the source and driving inputs. We follow five different cases to study the influence of motion and appearance on personality perception. Our comparative study reveals that motion and appearance influence different factors: motion strongly affects perceived extraversion, and appearance helps convey agreeableness and neuroticism.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 92 references that are in the map. First posted on arXiv 26 Jan 2024.
Builds on
29 of 92 references.
References · 29
- 2022Human Motion Diffusion Modelcited by 1,062
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
- 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
- 2020First Order Motion Model for Image Animationcited by 698
- 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
- 2021Advances in Neural Renderingcited by 365
- 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
- 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 319
- 2018Everybody Dance Nowcited by 313
- 2019Few-Shot Adversarial Learning of Realistic Neural Talking Head Modelscited by 284
- 2021Motion Representations for Articulated Animationcited by 253
17 more
- 2020Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learningcited by 219
- 2022Thin-Plate Spline Motion Model for Image Animationcited by 209
- 2018Animating Arbitrary Objects via Deep Motion Transfercited by 206
- 2023GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latentscited by 156
- 2019Progressive Pose Attention Transfer for Person Image Generationcited by 138
- 2020HeadGAN: One-shot Neural Head Synthesis and Editingcited by 105
- 2020Deep Image Spatial Transformation for Person Image Generationcited by 100
- 2019MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targetscited by 90
- 2018Recycle-GAN: Unsupervised Video Retargetingcited by 89
- 2022ZeroEGGS: Zero-shot Example-based Gesture Generation from Speechcited by 83
- 20193D Guided Fine-Grained Face Manipulationcited by 45
- 2020TransMoMo: Invariance-Driven Unsupervised Video Motion Retargetingcited by 36
- 2021Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesiscited by 16
- 2022Evaluating Data-Driven Co-Speech Gestures of Embodied Conversational Agents through Real-Time Interactioncited by 3
- 20183D Face Synthesis Driven by Personality Impressioncited by 1
- 2020Personality-Driven Gaze Animation with Conditional Generative Adversarial Networkscited by 1
- 2021Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognitioncited by 1
Built on by
Citing papers per year: 2024: 1.
1 paper.