Search Results for author:
Found 7 papers, 5 papers with code
LiDAR-based 4D Occupancy Completion and Forecasting
In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework.
Among Us: Adversarially Robust Collaborative Perception by Consensus
This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus.
Learning Analytical Posterior Probability for Human Mesh Recovery
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
iVS-Net: Learning Human View Synthesis from Internet Videos
However, these methods usually rely on limited multi-view images typically collected in the studio or commercial high-quality 3D scans for training, which heavily prohibits their generalization capability for in-the-wild images.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
The key challenge of this problem is to efficiently match 2D observations across multiple views.
Ranked #2 on
3D Multi-Person Pose Estimation
on Shelf
Reconstructing 3D Human Pose by Watching Humans in the Mirror
In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror.
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
Ranked #11 on
3D Multi-Person Pose Estimation (absolute)
on MuPoTS-3D
