Search Results for author:
Found 8 papers, 4 papers with code
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.
A Low-Cost & Real-Time Motion Capture System
Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption.
On Coordinate Decoding for Keypoint Estimation Tasks
A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i. e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy.
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space.
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.
[Re] On end-to-end 6{DoF} object pose estimation and robustness to object scale
We communicated with the authors of [1] through GitHub, and we would like to thank them as they provided a fast and detailed response.
Self-Supervised Deep Depth Denoising
Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference.
An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing
The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways.
