Search Results for author:
Found 15 papers, 7 papers with code
RACon: Retrieval-Augmented Simulated Character Locomotion Control
The retriever searches motion experts from a user-specified database in a task-oriented fashion, which boosts the responsiveness to the user's control.
Tri-Modal Motion Retrieval by Learning a Joint Embedding Space
Information retrieval is an ever-evolving and crucial research domain.
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation
MPT embeds multimodal fusion information into each attention layer of the Transformer, allowing prompt information to participate in encoding textual features and being fused with multi-level textual information to obtain better multimodal fusion features.
Ranked #6 on
Emotion Recognition in Conversation
on IEMOCAP
Event-based Human Pose Tracking by Spiking Spatiotemporal Transformer
Motivated by the above mentioned issues, we present in this paper a dedicated end-to-end sparse deep learning approach for event-based pose tracking: 1) to our knowledge this is the first time that 3D human pose tracking is obtained from events only, thus eliminating the need of accessing to any frame-based images as part of input; 2) our approach is based entirely upon the framework of Spiking Neural Networks (SNNs), which consists of Spike-Element-Wise (SEW) ResNet and a novel Spiking Spatiotemporal Transformer; 3) a large-scale synthetic dataset is constructed that features a broad and diverse set of annotated 3D human motions, as well as longer hours of event stream data, named SynEventHPD.
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet
In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage.
Generating Diverse and Natural 3D Human Motions From Text
Automated generation of 3D human motions from text is a challenging problem.
Ranked #3 on
Motion Synthesis
on Inter-X
Action2video: Generating Videos of Human 3D Actions
Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
Human Pose and Shape Estimation from Single Polarization Images
This paper focuses on a new problem of estimating human pose and shape from single polarization images.
EventHPE: Event-based 3D Human Pose and Shape Estimation
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
3D Human Shape Reconstruction from a Polarization Image
Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
MarlRank: Multi-agent Reinforced Learning to Rank
By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.
A Regularized Opponent Model with Maximum Entropy Objective
In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality".
Learning to Communicate Implicitly By Actions
The auxiliary reward for communication is integrated into the learning of the policy module.
