Search Results for author:
Found 17 papers, 8 papers with code
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.
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.
TAVA: Template-free Animatable Volumetric Actors
Since TAVA does not require a body template, it is applicable to humans as well as other creatures such as animals.
LISA: Learning Implicit Shape and Appearance of Hands
This paper proposes a do-it-all neural model of human hands, named LISA.
BANMo: Building Animatable 3D Neural Models from Many Casual Videos
Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.
ContactOpt: Optimizing Contact to Improve Grasps
Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes.
ANR: Articulated Neural Rendering for Virtual Avatars
The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images.
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video.
Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild
The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras.
Long-term Human Motion Prediction with Scene Context
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
4D Visualization of Dynamic Events from Unconstrained Multi-View Videos
We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras.
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles
In this work, we develop a framework to fuse both the single-view feature tracks and multi-view detected part locations to significantly improve the detection, localization and reconstruction of moving vehicles, even in the presence of strong occlusions.
Self-supervised Multi-view Person Association and Its Applications
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams.
Spatiotemporal Bundle Adjustment for Dynamic 3D Reconstruction
In this paper, we present a spatiotemporal bundle adjustment approach that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating 3D static points, as well as subframe temporal alignment between cameras and estimating 3D trajectories of dynamic points.
