82 citations · 168 across the 23 of their papers we have counts for
19 papers · 1 filter
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Panagiotis P. Filntisis, George Retsinas, Radek Daněček +3
Recent frameworks like ToFu and TEMPEH provide an automated alternative to classical registration pipelines by predicting 3D meshes in dense semantic correspondence directly from c…
NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Ivan Kosmynin +40
This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction me…
VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction
Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos +1
Monocular head pose estimation is traditionally formulated as direct regression from a single image to an absolute pose. This paradigm forces the network to implicitly internalize…
Mind the Shape Gap: A Benchmark and Baseline for Deformation-Aware 6D Pose Estimation of Agricultural Produce
Nikolas Chatzis, Angeliki Tsinouka, Katerina Papadimitriou +7
Accurate 6D pose estimation for robotic harvesting is fundamentally hindered by the biological deformability and high intra-class shape variability of agricultural produce. Instanc…
Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization
Georgios Chatzichristodoulou, Niki Efthymiou, Panagiotis Filntisis +2
While three-dimensional (3D) shape and pose estimation is a highly researched area that has yielded significant advances, the resulting methods, despite performing well for the adu…
Instance-Level Composed Image Retrieval
Bill Psomas, George Retsinas, Nikos Efthymiadis +5
The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of h…
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data
Marios Glytsos, Panagiotis P. Filntisis, George Retsinas +1
Accurate 6D object pose estimation is essential for robotic grasping and manipulation, particularly in agriculture, where fruits and vegetables exhibit high intra-class variability…
SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis
George Retsinas, Panagiotis P. Filntisis, Radek Danecek +4
While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely obse…
Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism
Georgios Milis, Panagiotis P. Filntisis, Anastasios Roussos +1
Recent advances in deep learning for sequential data have given rise to fast and powerful models that produce realistic videos of talking humans. The state of the art in talking fa…
Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models
Ioannis Pikoulis, Panagiotis P. Filntisis, Petros Maragos
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 rap…
Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective
Nikolaos Kegkeroglou, Panagiotis P. Filntisis, Petros Maragos
The COVID-19 pandemic has undoubtedly changed the standards and affected all aspects of our lives, especially social communication. It has forced people to extensively wear medical…
Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting
Christina O. Tze, Panagiotis P. Filntisis, Athanasia-Lida Dimou +2
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 targe…
Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
Panagiotis P. Filntisis, George Retsinas, Foivos Paraperas-Papantoniou +3
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. However, it has mostly…
Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos
Foivos Paraperas Papantoniou, Panagiotis P. Filntisis, Petros Maragos +1
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. The proposed method is based on…
An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild
Panagiotis Antoniadis, Ioannis Pikoulis, Panagiotis P. Filntisis +1
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…
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
Panagiotis Antoniadis, Panagiotis P. Filntisis, Petros Maragos
Over the past few years, deep learning methods have shown remarkable results in many face-related tasks including automatic facial expression recognition (FER) in-the-wild. Meanwhi…
Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild
Ioannis Pikoulis, Panagiotis P. Filntisis, Petros Maragos
In this work we tackle the task of video-based visual emotion recognition in the wild. Standard methodologies that rely solely on the extraction of bodily and facial features often…
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss
Panagiotis Paraskevas Filntisis, Niki Efthymiou, Gerasimos Potamianos +1
We present our winning submission to the First International Workshop on Bodily Expressed Emotion Understanding (BEEU) challenge. Based on recent literature on the effect of contex…
Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction
Panagiotis P. Filntisis, Niki Efthymiou, Petros Koutras +2
In this paper we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction. Towards this goal we propose a method for automatic r…