233 citations · 780 across the 34 of their papers we have counts for
39 papers
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…
Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
Zihan Huang, Junda Wu, Tong Yu +6
While LLM-based agents excel at planning and executing long action sequences, their execution often remains inconsistent across trials, limiting reliability. Consolidating agent co…
Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Benchmark and Evaluation
Yeonjun In, Mehrab Tanjim, Jayakumar Subramanian +6
Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks and methods largely assume a single deterministic root cause for ea…
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…
Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models
Fadlullah Raji, Stefano Petrangeli, Matheus Gadelha +3
Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing me…
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
Vijeta Deshpande, Ishita Dasgupta, Uttaran Bhattacharya +3
Synthetic text generated by Large Language Models (LLMs) is increasingly used for further training and improvement of LLMs. Diversity is crucial for the effectiveness of synthetic…
Quantitative LLM Judges
Aishwarya Sahoo, Jeevana Kruthi Karnuthala, Tushar Parmanand Budhwani +9
LLM-as-a-judge is a framework where a large language model (LLM) evaluates the output of another LLM. While LLMs excel at producing qualitative textual evaluations, they often stru…
Evaluation and Incident Prevention in an Enterprise AI Assistant
Akash V. Maharaj, David Arbour, Daniel Lee +6
Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a co…
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…
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
Md Mehrab Tanjim, Xiang Chen, Victor S. Bursztyn +8
Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we…
Evaluation and Continual Improvement for an Enterprise AI Assistant
Akash V. Maharaj, Kun Qian, Uttaran Bhattacharya +8
The development of conversational AI assistants is an iterative process with multiple components. As such, the evaluation and continual improvement of these assistants is a complex…
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…
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Ashmit Khandelwal, Aditya Agrawal, Aanisha Bhattacharyya +8
Shannon and Weaver's seminal information theory divides communication into three levels: technical, semantic, and effectiveness. While the technical level deals with the accurate r…
DanceAnyWay: Synthesizing Beat-Guided 3D Dances with Randomized Temporal Contrastive Learning
Aneesh Bhattacharya, Manas Paranjape, Uttaran Bhattacharya +1
We present DanceAnyWay, a generative learning method to synthesize beat-guided dances of 3D human characters synchronized with music. Our method learns to disentangle the dance mov…
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…
Speech2AffectiveGestures: Synthesizing Co-Speech Gestures with Generative Adversarial Affective Expression Learning
Uttaran Bhattacharya, Elizabeth Childs, Nicholas Rewkowski +1
We present a generative adversarial network to synthesize 3D pose sequences of co-speech upper-body gestures with appropriate affective expressions. Our network consists of two com…
Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents
Uttaran Bhattacharya, Nicholas Rewkowski, Abhishek Banerjee +3
We present Text2Gestures, a transformer-based learning method to interactively generate emotive full-body gestures for virtual agents aligned with natural language text inputs. Our…
Generating Emotive Gaits for Virtual Agents Using Affect-Based Autoregression
Uttaran Bhattacharya, Nicholas Rewkowski, Pooja Guhan +4
We present a novel autoregression network to generate virtual agents that convey various emotions through their walking styles or gaits. Given the 3D pose sequences of a gait, our…
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…