A few thoughts as I've done academic research & built products in this area:
- if you're using SMPL body parameters this will have to stay research / open-source
- is this leveraging some sort of monocular depth estimation to estimate the wall in 3D space? Also, do you have assumed camera parameters, or is that also estimated? If there isn't any depth information, this will be highly inaccurate on any cliff routes, but still useful on flat wall climbing.
Overall, a good idea (that I've also thought about building as a climber) - the tricky part that I'm impressed you have a solution to is path planning up the wall. Even assuming a flat wall with no depth estimation, it's still looks effective.
This is an end-end system that just takes in video frames. Camera parameters are one of the things that is predicted. It gives promising results for a wide variety of environments (cliffs, diff types of bouldering walls, diff outdoor walls, etc.), though not always accurate. Path planning is also part of the end-end system. Will share more details in the paper.
commercial license - the research group formed a corporate entity that licenses the body model and all derived work (SMPL-X, etc.): https://meshcapade.com/SMPL
- if you're using SMPL body parameters this will have to stay research / open-source - is this leveraging some sort of monocular depth estimation to estimate the wall in 3D space? Also, do you have assumed camera parameters, or is that also estimated? If there isn't any depth information, this will be highly inaccurate on any cliff routes, but still useful on flat wall climbing.
Overall, a good idea (that I've also thought about building as a climber) - the tricky part that I'm impressed you have a solution to is path planning up the wall. Even assuming a flat wall with no depth estimation, it's still looks effective.