Search Results for author:
Found 19 papers, 10 papers with code
Real-Time Simulated Avatar from Head-Mounted Sensors
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets.
Multi-Object Tracking by Hierarchical Visual Representations
We propose a new visual hierarchical representation paradigm for multi-object tracking.
Mixed Gaussian Flow for Diverse Trajectory Prediction
However, mapping from a standard Gaussian by a flow-based model hurts the capacity to capture complicated patterns of trajectories, ignoring the under-represented motion intentions in the training data.
Universal Humanoid Motion Representations for Physics-Based Control
We close this gap by significantly increasing the coverage of our motion representation space.
Unified Human-Scene Interaction via Prompted Chain-of-Contacts
Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution.
Perpetual Humanoid Control for Real-time Simulated Avatars
We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e. g. pose estimates from video or generated from language) and unexpected falls.
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training
This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset.
Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification
Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors.
Ranked #6 on
Multi-Object Tracking
on MOT17
(using extra training data)
Track Targets by Dense Spatio-Temporal Position Encoding
Experiments on video multi-object tracking (MOT) and multi-object tracking and segmentation (MOTS) datasets demonstrate the effectiveness of the proposed DST position encoding.
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+2
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empirically.
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Instead of relying only on the linear state estimate (i. e., estimation-centric approach), we use object observations (i. e., the measurements by object detector) to compute a virtual trajectory over the occlusion period to fix the error accumulation of filter parameters during the occlusion period.
Ranked #2 on
Multiple Object Tracking
on KITTI Tracking test
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association.
Disentangling Properties of Contrastive Methods
In this paper, we explored the possibility of using contrastive methods to learn a disentangled representation, a discriminative approach that is drastically different from previous approaches.
Instance-Aware Predictive Navigation in Multi-Agent Environments
To decide the action at each step, we seek the action sequence that can lead to safe future states based on the prediction module outputs by repeatedly sampling likely action sequences.
TransTrack: Multiple Object Tracking with Transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.
Ranked #7 on
Multi-Object Tracking
on SportsMOT
(using extra training data)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+3
Attribute Restoration Framework for Anomaly Detection
We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.
Ranked #21 on
Anomaly Detection
on One-class CIFAR-10
Cross-Domain Adaptation for Animal Pose Estimation
Therefore, the easily available human pose dataset, which is of a much larger scale than our labeled animal dataset, provides important prior knowledge to boost up the performance on animal pose estimation.
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency
To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text.
Pairwise Body-Part Attention for Recognizing Human-Object Interactions
We propose a new pairwise body-part attention model which can learn to focus on crucial parts, and their correlations for HOI recognition.
Ranked #5 on
Human-Object Interaction Detection
on HICO
