Search Results for author:
Found 16 papers, 8 papers with code
in2IN: Leveraging individual Information to Generate Human INteractions
For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction.
Ranked #1 on
Motion Synthesis
on InterHuman
(using extra training data)
Seamless Human Motion Composition with Blended Positional Encodings
Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics.
REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour.
Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach
The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging.
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions.
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction
To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion.
Ranked #1 on
Human Pose Forecasting
on AMASS
(ADE metric)
Towards Self-Supervised Gaze Estimation
Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification.
Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios
In this work, we present the first systematic comparison of state-of-the-art approaches for behavior forecasting.
Didn't see that coming: a survey on non-verbal social human behavior forecasting
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years.
Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions
Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for.
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload.
Person Perception Biases Exposed: Revisiting the First Impressions Dataset
This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing.
Benefits of temporal information for appearance-based gaze estimation
The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured.
OpenEDS2020: Open Eyes Dataset
We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals
Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions.
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction.
Ranked #1 on
Gaze Estimation
on EYEDIAP (screen target)
