Search Results for author:
Found 38 papers, 16 papers with code
Laying the Foundations of Deep Long-Term Crowd Flow Prediction
Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.
Less is More: Improving Motion Diffusion Models with Sparse Keyframes
This severely limits the performance of generative motion models for downstream tasks.
Cardiverse: Harnessing LLMs for Novel Card Game Prototyping
The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation.
CASIM: Composite Aware Semantic Injection for Text to Motion Generation
Recent advances in generative modeling and tokenization have driven significant progress in text-to-motion generation, leading to enhanced quality and realism in generated motions.
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications.
From Words to Worlds: Transforming One-line Prompt into Immersive Multi-modal Digital Stories with Communicative LLM Agent
Digital storytelling, essential in entertainment, education, and marketing, faces challenges in production scalability and flexibility.
An Intrinsic Vector Heat Network
Vector fields are widely used to represent and model flows for many science and engineering applications.
On the Equivalency, Substitutability, and Flexibility of Synthetic Data
In addition, our study of the impact of synthetic data distributions on downstream performance reveals the importance of flexible data generators in narrowing domain gaps for improved model adaptability.
Learning from Synthetic Human Group Activities
The study of complex human interactions and group activities has become a focal point in human-centric computer vision.
The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents
We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect.
M3Act: Learning from Synthetic Human Group Activities
The study of complex human interactions and group activities has become a focal point in human-centric computer vision.
Procedure-Aware Pretraining for Instructional Video Understanding
This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks.
MSI: Maximize Support-Set Information for Few-Shot Segmentation
To extract information relevant to the target class, a dominant approach in best-performing FSS methods removes background features using a support mask.
Ranked #3 on
Few-Shot Semantic Segmentation
on FSS-1000 (1-shot)
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning.
Optimizing Indoor Navigation Policies For Spatial Distancing
In this paper, we focus on the modification of policies that can lead to movement patterns and directional guidance of occupants, which are represented as agents in a 3D simulation engine.
HM: Hybrid Masking for Few-Shot Segmentation
A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects.
Ranked #5 on
Few-Shot Semantic Segmentation
on FSS-1000 (1-shot)
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction
Accurate long-term trajectory prediction in complex scenes, where multiple agents (e. g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem.
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects.
Ranked #2 on
Group Activity Recognition
on Collective Activity
Cross-Modal Coherence for Text-to-Image Retrieval
Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model.
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
We evaluate over CATER dataset and find that Hopper achieves 73. 2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames.
Ranked #5 on
Video Object Tracking
on CATER
AESOP: Abstract Encoding of Stories, Objects, and Pictures
Visual storytelling and story comprehension are uniquely human skills that play a central role in how we learn about and experience the world.
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans
We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes.
GitEvolve: Predicting the Evolution of GitHub Repositories
Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge
Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student.
Deep Crowd-Flow Prediction in Built Environments
In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.
Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures
In this paper, we propose an agent-based simulation tool, which is grounded in human cognition and decision-making, for evaluating and improving the effectiveness of building evacuation procedures and guidance systems during a disaster.
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation
Multi-agent reinforcement learning is a particularly challenging problem.
Generating Animations from Screenplays
However, translating natural language text into animation is a challenging task.
Semantic Graph Convolutional Networks for 3D Human Pose Regression
In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression.
Ranked #27 on
Monocular 3D Human Pose Estimation
on Human3.6M
Domain Authoring Assistant for Intelligent Virtual Agents
The process of creating such characters often involves a team of creative authors who describe different aspects of the characters in natural language, and planning experts that translate this description into a planning domain.
Affect-Driven Dialog Generation
The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses.
Topic Spotting using Hierarchical Networks with Self Attention
Additionally, in contrast to offline processing of dialog, we also analyze the performance of our model in a more realistic setting i. e. in an online setting where the topic is identified in real time as the dialog progresses.
Disney at IEST 2018: Predicting Emotions using an Ensemble
This paper describes our participating system in the WASSA 2018 shared task on emotion prediction.
Learning to Forecast and Refine Residual Motion for Image-to-Video Generation
We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.
Cartoonish sketch-based face editing in videos using identity deformation transfer
We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.
