212 citations · 361 across the 30 of their papers we have counts for
33 papers
CCDF: A Benchmark Dataset for Deepfake Detection in Surveillance Footage
Baptiste Chopin, Thomas Swearingen, Arun Ross +2
Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both human viewers and automated synt…
BEACON: Behavior and Appearance Control for Subject-Specific Video Generation
Pokrzywa Baptiste, Nabyl Quignon, Yara Bahram +3
Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In addition to reproducing appearance, a…
DenVisCoM: Dense Vision Correspondence Mamba for Efficient and Real-time Optical Flow and Stereo Estimation
Tushar Anand, Maheswar Bora, Antitza Dantcheva +1
In this work, we propose a novel Mamba block DenVisCoM, as well as a novel hybrid architecture specifically tailored for accurate and real-time estimation of optical flow and dispa…
Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
Anil Egin, Andrea Tangherloni, Antitza Dantcheva
Face video anonymization is aimed at privacy preservation while allowing for the analysis of videos in a number of computer vision downstream tasks such as expression recognition,…
AI killed the video star. Audio-driven diffusion model for expressive talking head generation
Baptiste Chopin, Tashvik Dhamija, Pranav Balaji +2
We propose Dimitra++, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we…
Beyond Real versus Fake Towards Intent-Aware Video Analysis
Saurabh Atreya, Nabyl Quignon, Baptiste Chopin +2
The rapid advancement of generative models has led to increasingly realistic deepfake videos, posing significant societal and security risks. While existing detection methods focus…
Do You See What I Say? Generalizable Deepfake Detection based on Visual Speech Recognition
Maheswar Bora, Tashvik Dhamija, Shukesh Reddy +4
Deepfake generation has witnessed remarkable progress, contributing to highly realistic generated images, videos, and audio. While technically intriguing, such progress has raised…
THEval. Evaluation Framework for Talking Head Video Generation
Nabyl Quignon, Baptiste Chopin, Yaohui Wang +1
Video generation has achieved remarkable progress, with generated videos increasingly resembling real ones. However, the rapid advance in generation has outpaced the development of…
LIA-X: Interpretable Latent Portrait Animator
Yaohui Wang, Di Yang, Xinyuan Chen +3
We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an au…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…
Dimitra: Audio-driven Diffusion model for Expressive Talking Head Generation
Baptiste Chopin, Tashvik Dhamija, Pranav Balaji +2
We propose Dimitra, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we tr…
MVP: Multimodal Emotion Recognition based on Video and Physiological Signals
Valeriya Strizhkova, Hadi Kachmar, Hava Chaptoukaev +9
Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using class…
AM Flow: Adapters for Temporal Processing in Action Recognition
Tanay Agrawal, Abid Ali, Antitza Dantcheva +1
Deep learning models, in particular \textit{image} models, have recently gained generalisability and robustness. %are becoming more general and robust by the day. In this work, we…
GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields
Arnab Dey, Di Yang, Rohith Agaram +4
Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations…
HFNeRF: Learning Human Biomechanic Features with Neural Radiance Fields
Arnab Dey, Di Yang, Antitza Dantcheva +1
In recent advancements in novel view synthesis, generalizable Neural Radiance Fields (NeRF) based methods applied to human subjects have shown remarkable results in generating nove…
Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning
Pranav Balaji, Abhijit Das, Srijan Das +1
This work explores various ways of exploring multi-task learning (MTL) techniques aimed at classifying videos as original or manipulated in cross-manipulation scenario to attend ge…
LAC: Latent Action Composition for Skeleton-based Action Segmentation
Di Yang, Yaohui Wang, Antitza Dantcheva +4
Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features f…
Self-Supervised Video Representation Learning via Latent Time Navigation
Di Yang, Yaohui Wang, Quan Kong +4
Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. Th…
LEO: Generative Latent Image Animator for Human Video Synthesis
Yaohui Wang, Xin Ma, Xinyuan Chen +4
Spatio-temporal coherency is a major challenge in synthesizing high quality videos, particularly in synthesizing human videos that contain rich global and local deformations. To re…
Learning Invariance from Generated Variance for Unsupervised Person Re-identification
Hao Chen, Yaohui Wang, Benoit Lagadec +2
This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the…
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Di Yang, Yaohui Wang, Antitza Dantcheva +3
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory setti…
Synthetic Data in Human Analysis: A Survey
Indu Joshi, Marcel Grimmer, Christian Rathgeb +3
Deep neural networks have become prevalent in human analysis, boosting the performance of applications, such as biometric recognition, action recognition, as well as person re-iden…
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
Yaohui Wang, Di Yang, Francois Bremond +1
Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current…
Beyond the Visible: A Survey on Cross-spectral Face Recognition
David Anghelone, Cunjian Chen, Arun Ross +1
Cross-spectral face recognition (CFR) refers to recognizing individuals using face images stemming from different spectral bands, such as infrared versus visible. While CFR is inhe…