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mtamis-NV avatar mtamis-NV commented on August 19, 2024

I'm not sure if I understand what you are trying to do. Are you trying to simulate multiple cloth layers on top of each other? Or are you trying to stitch two pieces of cloth together side by side?

The latter case could be done by merging the different pieces of cloth into a single cloth object.

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ghedin avatar ghedin commented on August 19, 2024

Hello! Thank you so much for your prompt reply.

I would like to stitch two pieces of cloth together side by side - but there's one restriction that prevents me from merging them into a single cloth object: I would need to use a different Fabric for each Cloth (i.e.: simulate two different types of fabric, like, cloth "a" looks like cotton and cloth "b" looks like jeans).

Thank you once again.

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mtamis-NV avatar mtamis-NV commented on August 19, 2024

Which parameters are you using to make the jeans and the cotton behave different?

We are probably not going to add support for constraints between two different cloth objects, as that would be complicated and slow.
However we might be able to make the cloth/fabric more flexible so that the parameters can vary within a single cloth.

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ghedin avatar ghedin commented on August 19, 2024

The only parameter I could use so far is the stiffness, which can be applied per particle. If we could do the same with stretching, bending and shearing for example, I guess it would already be possible to simulate different types of materials. Other parameters of interest are the static & dynamic friction.

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mtamis-NV avatar mtamis-NV commented on August 19, 2024

Maybe you can merge the two pieces of cloth, cook them together and split the constraint phases. That way you can assign different phase properties for the different cloth pieces.

This would require you to write your own cooking code (as opposed to using the NvClothCookFabricFromMesh helper function).

We currently don't support what you want to do in another way.

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ghedin avatar ghedin commented on August 19, 2024

That sounds good - we'll try it in this way. Thanks a lot for your support! 👍

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