3 citations · 11 across the 30 of their papers we have counts for
13 papers · 1 filter
A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques
Lownish Rai Sookha, Nikhil Pakhale, Mudasir Ganaie +1
Body and face motion play an integral role in communication. They convey crucial information on the participants. Advances in generative modeling and multi-modal learning have enab…
CSGaze: Context-aware Social Gaze Prediction
Surbhi Madan, Shreya Ghosh, Ramanathan Subramanian +2
A person's gaze offers valuable insights into their focus of attention, level of social engagement, and confidence. In this work, we investigate how contextual cues combined with v…
A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics
Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall +1
Visual reasoning, particularly spatial reasoning, is a challenging cognitive task that requires understanding object relationships and their interactions within complex environment…
SFANet: Spatial-Frequency Attention Network for Deepfake Detection
Vrushank Ahire, Aniruddh Muley, Shivam Zample +4
Detecting manipulated media has now become a pressing issue with the recent rise of deepfakes. Most existing approaches fail to generalize across diverse datasets and generation te…
SynchroRaMa : Lip-Synchronized and Emotion-Aware Talking Face Generation via Multi-Modal Emotion Embedding
Phyo Thet Yee, Dimitrios Kollias, Sudeepta Mishra +1
Audio-driven talking face generation has received growing interest, particularly for applications requiring expressive and natural human-avatar interaction. However, most existing…
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Ganesh Krishnasamy +2
Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed t…
LayLens: Improving Deepfake Understanding through Simplified Explanations
Abhijeet Narang, Parul Gupta, Liuyijia Su +1
This demonstration paper presents , a tool aimed to make deepfake understanding easier for users of all educational backgrounds. While prior works often rely on o…
AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations
Zhixi Cai, Kartik Kuckreja, Shreya Ghosh +5
The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in…
Improving Personalized Image Generation through Social Context Feedback
Parul Gupta, Abhinav Dhall, Thanh-Toan Do
Personalized image generation, where reference images of one or more subjects are used to generate their image according to a scene description, has gathered significant interest i…
Gems: Group Emotion Profiling Through Multimodal Situational Understanding
Anubhav Kataria, Surbhi Madan, Shreya Ghosh +2
Understanding individual, group and event level emotions along with contextual information is crucial for analyzing a multi-person social situation. To achieve this, we frame emoti…
Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
Parul Gupta, Shreya Ghosh, Tom Gedeon +2
The rapid advancement of GenAI technology over the past few years has significantly contributed towards highly realistic deepfake content generation. Despite ongoing efforts, the r…
Tell me Habibi, is it Real or Fake?
Kartik Kuckreja, Parul Gupta, Injy Hamed +3
Deepfake generation methods are evolving fast, making fake media harder to detect and raising serious societal concerns. Most deepfake detection and dataset creation research focus…
MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network
Vrushank Ahire, Kunal Shah, Mudasir Nazir Khan +4
Dynamic emotion recognition in the wild remains challenging due to the transient nature of emotional expressions and temporal misalignment of multi-modal cues. Traditional approach…