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Found 16 papers, 6 papers with code
URAvatar: Universal Relightable Gaussian Codec Avatars
Unlike existing approaches that estimate parametric reflectance parameters via inverse rendering, our approach directly models learnable radiance transfer that incorporates global light transport in an efficient manner for real-time rendering.
Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions
Understanding how humans interact with each other is key to building realistic multi-human virtual reality systems.
Sapiens: Foundation for Human Vision Models
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
Ranked #1 on
Keypoint Detection
on MS COCO
(Validation AP metric, using extra
training data)
Generalizable Neural Human Renderer
While recent advancements in animatable human rendering have achieved remarkable results, they require test-time optimization for each subject which can be a significant limitation for real-world applications.
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-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras
In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated.
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 #10 on
Semantic Segmentation
on PASCAL Context
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 CroHD
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 #74 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 #5 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.
