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Found 8 papers, 3 papers with code
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions.
3D human pose and shape estimation
Markerless Motion Capture
Noise-in, Bias-out: Balanced and Real-time MoCap Solving
By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data.
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture
In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images.
A Low-Cost & Real-Time Motion Capture System
Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption.
A benchmark with decomposed distribution shifts for 360 monocular depth estimation
In this work we contribute a distribution shift benchmark for a computer vision task; monocular depth estimation.
Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation
Pano3D is a new benchmark for depth estimation from spherical panoramas.
SHREC 2020 track: 6D Object Pose Estimation
From captured color and depth images, we use this simulator to generate a 3D dataset which has 400 photo-realistic synthesized color-and-depth image pairs with various view angles for training, and another 100 captured and synthetic images for testing.
DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss
In this context, the 3D localisation of the UAV assistant is an important task that can facilitate the exchange of spatial information between the user and the UAV.
Ranked #1 on
Drone Pose Estimation
on UAVA
