Search Results for author:
Found 10 papers, 4 papers with code
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
EgoHumans: An Egocentric 3D Multi-Human Benchmark
We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking.
Ego-Humans: An Ego-Centric 3D Multi-Human Benchmark
We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking.
Sequential Ensembling for Semantic Segmentation
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications.
Ranked #8 on
Semantic Segmentation
on Cityscapes val
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 #1 on
Multiple Object Tracking
on KITTI Tracking test
Occluded Human Mesh Recovery
Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps.
Ranked #54 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in.
Ranked #4 on
6D Pose Estimation using RGB
on LineMOD
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Specifically, we achieve 70. 0 AP on CrowdPose and 42. 5 AP on OCHuman test sets, a significant improvement of 2. 4 AP and 6. 5 AP over the prior art, respectively.
Ranked #1 on
Multi-Person Pose Estimation
on OCHuman
Adversarial Domain Randomization
Domain Randomization (DR) is known to require a significant amount of training data for good performance.
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator.
