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Found 12 papers, 9 papers with code
3D Facial Expressions through Analysis-by-Neural-Synthesis
Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the rendered predicted mesh geometry, and sparsely sampled pixels of the input image, generates a face image.
Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism
Our method, which we call NEUral Text to ARticulate Talk (NEUTART), is a talking face generator that uses a joint audiovisual feature space, as well as speech-informed 3D facial reconstructions and a lip-reading loss for visual supervision.
Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models
In this paper, we investigate the emotion manipulation capabilities of diffusion models with "in-the-wild" images, a rather unexplored application area relative to the vast and rapidly growing literature for image-to-image translation tasks.
Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective
In this paper, we conduct insightful studies about the effect of face occlusion on emotion recognition performance, and showcase the superiority of full body input over the plain masked face.
Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting
In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video.
Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning.
Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos
In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in "in-the-wild" videos.
An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild
In this work we tackle the task of video-based audio-visual emotion recognition, within the premises of the 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW2).
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
To evaluate the performance of our method under real-world conditions we perform extensive experiments on the AffectNet and Aff-Wild2 datasets.
Ranked #9 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric)
Facial Expression Recognition
Facial Expression Recognition (FER)
Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild
In this work we tackle the task of video-based visual emotion recognition in the wild.
Ranked #3 on
Emotion Recognition in Context
on BoLD
ChildBot: Multi-Robot Perception and Interaction with Children
In this paper we present an integrated robotic system capable of participating in and performing a wide range of educational and entertainment tasks, in collaboration with one or more children.
Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction
In this paper we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction.
