AvatarPoser
unsupervised-depth-completion-visual-inertial-odometry
AvatarPoser | unsupervised-depth-completion-visual-inertial-odometry | |
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3 | 2 | |
274 | 185 | |
2.6% | - | |
2.8 | 5.0 | |
about 2 months ago | 10 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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AvatarPoser
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AvatarPoser - full body pose tracking from nothing but the 6D input of headset and controllers or hands
https://github.com/eth-siplab/AvatarPoser I see legs moving. I see squatting, sitting, the lot. This definitely won't be a practical use case for a while, but I don't know how this is any less FBT than a Kinect camera.
unsupervised-depth-completion-visual-inertial-odometry
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Unsupervised Depth Completion from Visual Inertial Odometry
Hey there, interested in camera and range sensor fusion for point cloud (depth) completion?
Here is an extended version of our [talk](https://www.youtube.com/watch?v=oBCKO4TH5y0) at ICRA 2020 where we do a step by step walkthrough of our paper Unsupervised Depth Completion from Visual Inertial Odometry (joint work with Fei Xiaohan, Stephanie Tsuei, and Stefano Soatto).
In this talk, we present an unsupervised method (no need for human supervision/annotations) for learning to recover dense point clouds from images, captured by cameras, and sparse point clouds, produced by lidar or tracked by visual inertial odometry (VIO) systems. To illustrate what I mean, here is an [example](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry/blob/master/figures/void_teaser.gif?raw=true) of the point clouds produced by our method.
Our method is light-weight (so you can run it on your computer!) and is built on top of [XIVO] (https://github.com/ucla-vision/xivo) our VIO system.
For those interested here are links to the [paper](https://arxiv.org/pdf/1905.08616.pdf), [code](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry) and the [dataset](https://github.com/alexklwong/void-dataset) we collected.
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[N][R] ICRA 2020 extended talk for Unsupervised Depth Completion from Visual Inertial Odometry
In this talk, we present an unsupervised method (no need for human supervision/annotations) for learning to recover dense point clouds from images, captured by cameras, and sparse point clouds, produced by lidar or tracked by visual inertial odometry (VIO) systems. To illustrate what I mean, you can visit our github page for examples (gifs) of point clouds produced by our method.
What are some alternatives?
metrabs - Estimate absolute 3D human poses from RGB images.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Make-It-3D - [ICCV 2023] Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
blender-retarget - Applies animation from one armature to another
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
awesome-holography - A curated list of resources on holographic displays.
xivo - X Inertial-aided Visual Odometry
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)