Comments (2)
Using 3D joint and human-computer interaction control, such as Nreal AR glasses.
https://www.youtube.com/watch?v=9LxOlsHu3r8&ab_channel=UploadVR
If there is no absolute depth, it is impossible to judge whether the button has been touched.
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Hello there
Since we use a single cropped RGB image as input, it is difficult to find the absolute 3D coordinate in our pipeline.
My suggestion is try to calculate relative depth. You could approximate the relative depth using prior information. In many cases, you should know the bone length (person height/finger length). Given x, y location in the image, the bone length and focal length, you could calculate the relative depth using similar triangles. You can find useful discussions in Prof. Kanazawa's post here: akanazawa/hmr#81
now with the relative 3D coordinate, to find the final 3D coordinate, you can approximate it by adding the 3D camera location to the root of the relative 3D coordinate. In general case, the AR device should have sensors to provide the camera location. This may not give you a perfect 3D coordinate, but at least you have an approximation.
there are many other good solutions. Some examples can be found here:
https://arxiv.org/pdf/1804.09534.pdf
https://arxiv.org/pdf/1907.11346.pdf
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Related Issues (20)
- This repo is missing important files
- How to visualize attention correlation as the paper
- Training on single dataset HOT 1
- Output Score / Confidence
- Project dependencies may have API risk issues
- Cannot render my hand by my photo HOT 1
- About the template vertices and joints
- Question about training with hand dataset? HOT 1
- Can't download 'https://datarelease.blob.core.windows.net/metro/datasets/filename.tar'
- pre-trained models!ERROR 400: Bad Request.
- Demo Lack of three-digit reconstruction effect,缺少三位重建 HOT 1
- COCO SMPL Data Missing
- About DDP train on the specified gpu
- It is stopped in 2023-10-31 16:00:44,169 METRO Inference INFO: Using 1 GPUs
- Exception: Unable to get url: http://files.is.tue.mpg.de/mloper/opendr/osmesa/OSMesa.Linux.aarch64.zip HOT 1
- Question about the learning rate adjustment strategy
- Question about weight decay setting
- Pre-trained model licensing
- How to do single-player multi-card training? HOT 1
- Apex version incompatible HOT 2
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