233 citations · 780 across the 34 of their papers we have counts for
18 papers · 1 filter
Spend Where It Matters: Particle Search with MH Refinement for Budget-Constrained Audio-Visual Alignment
Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang +1
Reward-guided inference-time alignment can improve joint audio-visual generation, but optimizing multiple alignment objectives requires many expensive model evaluations, making exi…
GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling
Shivanshu Shekhar, Uttaran Bhattacharya, Raghavendra Addanki +3
Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle…
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions
Ting-Hsuan Liao, Yi Zhou, Yu Shen +4
We explore how body shapes influence human motion synthesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of learning a homogenized, can…
HUMOTO: A 4D Dataset of Mocap Human Object Interactions
Jiaxin Lu, Chun-Hao Paul Huang, Uttaran Bhattacharya +2
We present Human Motions with Objects (HUMOTO), a high-fidelity dataset of human-object interactions for motion generation, computer vision, and robotics applications. Featuring 73…
SimMotionEdit: Text-Based Human Motion Editing with Motion Similarity Prediction
Zhengyuan Li, Kai Cheng, Anindita Ghosh +3
Text-based 3D human motion editing is a critical yet challenging task in computer vision and graphics. While training-free approaches have been explored, the recent release of the…
Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs
Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
We present a multimodal learning-based method to simultaneously synthesize co-speech facial expressions and upper-body gestures for digital characters using RGB video data captured…
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances
Supreeth Narasimhaswamy, Uttaran Bhattacharya, Xiang Chen +3
Text-to-image generative models can generate high-quality humans, but realism is lost when generating hands. Common artifacts include irregular hand poses, shapes, incorrect number…
VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding
Yizhou Wang, Ruiyi Zhang, Haoliang Wang +3
Recent advancements in language-model-based video understanding have been progressing at a remarkable pace, spurred by the introduction of Large Language Models (LLMs). However, th…
Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention
Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli +2
We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our m…
HighlightMe: Detecting Highlights from Human-Centric Videos
Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli +2
We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multipl…
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders
Abhishek Banerjee, Uttaran Bhattacharya, Aniket Bera
We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in trainin…
EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle
Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya +3
We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology…
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues
Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra +2
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the…
The Liar's Walk: Detecting Deception with Gait and Gesture
Tanmay Randhavane, Uttaran Bhattacharya, Kyra Kapsaskis +3
We present a data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures. We conducted an elaborate user study, where we…
Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping
Uttaran Bhattacharya, Christian Roncal, Trisha Mittal +5
We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as se…
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra +3
We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Gi…
Identifying Emotions from Walking using Affective and Deep Features
Tanmay Randhavane, Uttaran Bhattacharya, Kyra Kapsaskis +3
We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we ex…
Efficient and Robust Registration on the 3D Special Euclidean Group
Uttaran Bhattacharya, Venu Madhav Govindu
We present an accurate, robust and fast method for registration of 3D scans. Our motion estimation optimizes a robust cost function on the intrinsic representation of rigid motions…