[prions]

It looks really interesting, but as an experienced climber I'm not sure if just watching a video of my avatar climbing would really help with skill acquisition.

Also, this claims that the wall type or video quality doesn't matter, but I have a hard time understanding how the model would be able to understand that a small crimp could possibly be dual textured and therefore has only a few specific ways of approaching it.

So it seems that this is more for visualizing a climb (which is a skill most climbers should develop) and not really for dialing in some sort of microbeta for a problem.

[petsfed]

I suspect that, absent that information about the exact right way to grab a hold, or the exact way to put a foot on a hold, you'll be limited to beta suggestions, which is fine, I think. It'd be like having a group of other climbers nearby to suggest different beta, even if you don't have any friends.

So, in terms of solving complicated beta faster, I see real utility to this.

It is very interesting that since the AI climber is trained on actual climbers, it could, in principle, provide beta to climb consistent with your own style. If you train the bot exclusively on footage of yourself, it would return movement based on your style. If your style is finessy-all-backstep-all-the-time (aka The Edlinger), it can provide beta consistent with that. If your style is to square up and pull (otherwise known as The American), it can provide beta consistent with that instead.

[bmj]

(Long time climber here)

> It is very interesting that since the AI climber is trained on actual climbers, it could, in principle, provide beta to climb consistent with your own style. If you train the bot exclusively on footage of yourself, it would return movement based on your style. If your style is finessy-all-backstep-all-the-time (aka The Edlinger), it can provide beta consistent with that. If your style is to square up and pull (otherwise known as The American), it can provide beta consistent with that instead.

I would think this is actually a Bad Thing. It's very easy to get stuck trying to make a sequence fit your style of climbing. The better approach (especially for long term skill acquisition) is a willingness to learn new styles. That's to say that every sequence is only solvable via one particular style, but I think long term development is hindered if you approach every crux with the one thing you are good at.

> So, in terms of solving complicated beta faster, I see real utility to this.

I can agree with this. But, to the point that others have made, I do wonder what this and the availability of beta videos for many, many routes and blocs does to climbing skill overall. Perhaps I'm just a grumpy old man, but, particularly when bouldering, sorting out the beta should be part of the journey toward eventually sending. Last fall, I visited Hueco Tanks after a six year absence. I suppose I was a bit disappointed to see so many people watching YouTube beta videos of nearly every problem they tried.

[petsfed]

>I can agree with this. But, to the point that others have made, I do wonder what this and the availability of beta videos for many, many routes and blocs does to climbing skill overall.

That's a fair concern. That said, there are certain sequences that I'm physically incapable of doing without a dedicated multi-month program of stretching beforehand. Turns out falling on a thumbs-down jam is close enough to a shoulder dislocation that I maybe should've done some PT about it. The extreme is, obviously, the Edlinger vs. American example, but I think the middle ground actually addresses people's peculiar body geometry and/or range of motion. Alex Honnold's exact hip or elbow position might not be as meaningful for Ashima, even if they're on the exact same route.

Such nuances have made a difference in some cases (the one that springs to mind is Todd Skinner's observation that Steve Petro's hips sagged just a little mid-crux on Fiddler on the Roof, which, until corrected, had prevented Petro from nabbing the first ascent of one of the hardest cracks ever climbed to that point). Probably a net-bad for folks projecting Midnight Lightning or similar, but definitely useful for somebody looking to repeat Silence or whatever.

[smandava]

Correct

[etrautmann]

Agreed - so much about the detail of how you would climb something comes down to details that would be hard to measure with a camera, like textures, your estimate of friction, etc. Very cool idea though, looks fun to test.

[sandworm101]

And this is deep into "sport climbing", borderline gym rat territory imho. It doesn't model all the other core aspects such as protection, rope management, exposure and rest stops. I imaging if you pointed this at a real cliff and recorded several assents it would quickly become a blurry-twitchy mess as all the movements not touching the rock spoiled the data. Maybe for bouldering, but not for real climbing.

[soperj]

What makes rope climbing "real"?

[lambdaone]

There are lots more variables to consider, particularly in lead climbing, even when you have a bolted route. And trad climbing is even more complex than that.

[sandworm101]

Trad lead is climbing. Basically everything else is some sort of simulation.

[et-al]

It's 2024 and some people are gonna gatekeep that if you're not shoving cams in some bigwall granite, it's not climbing?

[Snackchez]

What could be interesting is if you could compare your attempts to the avatar climbing and receiving feedback afterwards. This would effectively be a step up from simply recording your send attempts.

[logtempo]

I don't see the shape of the holds being a big problem. With some help from indoor companies and hold makers, figuring out which hold model is on the wall should be possible.

As for the usefullness of the software, I'm sceptical too as it don't really solve a problem. But maybe I'm not seeing it and it could be good for beginners :) A good improvement would be adding a comparison between you and the model in term of body position and fluidity of movements.

[party_possum]

The idea of incorporating actual hold data and "recognizing" specific holds is interesting, but I'm not sure it completely solves the problem.

The "Boss" from Pusher is arguably the most famous climbing hold ever made. For a decade or more, every gym had one, but they were all unique. Lots of them had micro chips that became critical to usage of the hold. Some had decent texture and some were glassy smooth from years and years and years of use. A lot of the accidental variation in new holds has gone away as the industry has standardized around a handful of industrial fabricators like Aragon, but even over the course of a single indoor boulder problem's life, the accumulation of chalk, sweat, and shoe rubber can have a significant impact on how a hold climbs.

I guess the real question is, do these changes just make routes harder or do they make them fundamentally different? Do they actually change the set of moves that constitutes the easiest way to the top? To be honest, I'm not entirely sure. But it's something interesting to think about.

[alexcp_]

Exactly, holds will evolve as they get used and more polished, even indoors. Climbing a Moonboard with a new set of holds is quite different than climbing on one with older more polished holds, even if it's the exact same problem and the same holds.

It's an interesting project and it could be fun to watch, but it's completely useless.

[ethbr1]

Couldn't you reverse-reason about that?

To me, the customer here would be climbing gyms, offering a service to climbers.

   1. Set up camera on routes
   2. Record all climbs
   3. Reason through hold details
   4. Generate potential movements
   5. Show climbs vs ghost movements
   6. Feedback to tune model

3 being accomplished by reasoning "if a movement should be possible using the identified hold, but no one successfully does it, the hold must be misidentified or have different properties."

[alexcp_]

But what is the point? Finding the optimal movements that are needed to complete a climb is not useful if you are not strong enough to execute it.

[ethbr1]

The point in this thread seemed to be "real world holds have different properties, and that defines possible approaches to holds."

To which I pointed out that, with enough data, you could reason backwards to figure out their properties.

Assuming that's solved, if the question is "What is the point?" then I'd answer the same point as golf swing analysis -- structured comparison feedback for continual improvement.

"Have you thought about trying X move at Y point?" or "You're trying X move at Y point, but here's how you differ from someone successfully doing it" both seem useful feedback.

And essentially what's manually generated now, from someone watching and then providing feedback.

With regards to strength, hell, if you wanted to get fancy you could also deduce a specific user's strength, comparing their moves against others' moves on the same features.

[jfengel]

Huh. I recognize it but didn't know its name. (I don't know any of the names.) Route setting sounds fascinating.

[Zanfa]

> I don't see the shape of the holds being a big problem. With some help from indoor companies and hold makers, figuring out which hold model is on the wall should be possible.

Even if you know the exact hold model and it’s in pristine condition, it’s basically impossible to tell how it’s gonna work from a single angle on video at a distance. Even tiny variations in angle of the wall and rotation of the hold on the wall can completely change how you use it.

[smandava]

To clarify:

Of the full distribution of possible video qualities one can take on a modern phone camera, the vast majority of video qualities will be fine for the AI to understand fine details. Obviously, if you somehow or for some reason, take a video with really bad quality, it will not give you what you want.

Same explanation goes for the walls. If you take a video of just a really dark wall with really bad holds, it is probably won't give you what you want either.

[smandava]

What do you think are some use cases where an avatar simulation of you can help (if any)?