Generating compound colliders
A method of generating a compound collider for a given object, the method comprising the steps of: receiving a mesh of the given object, the mesh comprising at least one concave portion; and generating a compound collider representative of at least the concave portion of the mesh, in which the step of generating comprises: inputting the mesh to a machine learning model trained to generate a respective compound collider based on an input mesh comprising at least one concave portion, and using the trained machine learning model to generate the compound collider representative of at least the concave portion of the mesh.
1 . A computer-implemented method of generating a compound collider for a given object, the method comprising:
receiving, by one or more processors, a mesh of the given object, the mesh comprising at least one concave portion;
receiving, by the one or more processors, information representative of a bone hierarchy of the given object;
generating, by the one or more processors, a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy; and
using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.
2 . The method of claim 1 , wherein the machine learning model is trained to generate the compound collider by associating at least one respective convex collider of the compound collider with a respective bone of the bone hierarchy of the given object.
3 . The method of claim 1 , wherein the information representative of the bone hierarchy includes information representative of a range of motion of respective joints comprised by the bone hierarchy.
4 . The method of claim 1 , wherein the information representative of the bone hierarchy includes information representative of one or more animations for the given object.
5 . The method of claim 4 , wherein the machine learning model is trained to generate, based on the mesh, the bone hierarchy, and the one or more animations, a respective compound collider optimized for the one or more animations for the given object.
6 . The method of claim 1 , wherein the machine learning model is configured to generate the compound collider by generating a plurality of convex colliders in an order based on a hierarchical structure of the bone hierarchy.
7 . The method of claim 6 , wherein the machine learning model generates a first convex collider corresponding to a root bone of the bone hierarchy before generating a second convex collider corresponding to a child bone of the root bone.
8 . The method of claim 1 , wherein the machine learning model is trained to generate the compound collider from a plurality of predefined convex colliders.
9 . The method of claim 8 , wherein the plurality of predefined convex colliders comprises a plurality of primitive colliders comprising at least one of a cuboid, a cylinder, or a sphere.
10 . The method of claim 1 , wherein the machine learning model is trained to generate the compound collider based on a given category of object.
11 . The method of claim 10 , wherein the given category of object comprises at least one of a user avatar, an interactable object, or a non-player character (NPC).
12 . A system comprising:
one or more computer processors; and
one or more storage devices storing instructions that, when executed by the one or more computer processors, cause the one or more computer processors to perform operations for generating a compound collider for a given object, the operations comprising:
receiving a mesh of the given object, the mesh comprising at least one concave portion;
receiving information representative of a bone hierarchy of the given object;
generating a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy; and
using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.
13 . The system of claim 12 , wherein the machine learning model is trained to generate the compound collider by associating at least one respective convex collider of the compound collider with a respective bone of the bone hierarchy of the given object.
14 . The system of claim 12 , wherein the information representative of the bone hierarchy includes information representative of a range of motion of respective joints comprised by the bone hierarchy.
15 . The system of claim 12 , wherein the information representative of the bone hierarchy includes information representative of one or more animations for the given object.
16 . The system of claim 15 , wherein the machine learning model is trained to generate, based on the mesh, the bone hierarchy, and the one or more animations, a respective compound collider optimized for the one or more animations for the given object.
17 . The system of claim 12 , wherein the machine learning model is configured to generate the compound collider by generating a plurality of convex colliders in an order based on a hierarchical structure of the bone hierarchy.
18 . The system of claim 17 , wherein the machine learning model generates a first convex collider corresponding to a root bone of the bone hierarchy before generating a second convex collider corresponding to a child bone of the root bone.
19 . The system of claim 12 , wherein the machine learning model is trained to generate the compound collider from a plurality of predefined convex colliders.
20 . One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more computer processors, cause the one or more computer processors to perform operations for generating a compound collider for a given object, the operations comprising:
receiving a mesh of the given object, the mesh comprising at least one concave portion;
receiving information representative of a bone hierarchy of the given object;
generating a compound collider representative of the at least one concave portion of the mesh, wherein the generating comprises:
inputting the mesh and the information representative of the bone hierarchy to a machine learning model,
wherein the machine learning model has been trained to learn a correspondence between an input mesh and an input representative of an input bone hierarchy to generate a respective compound collider configured to move with the input bone hierarchy; and
using the trained machine learning model to generate the compound collider based on a learned correspondence between the mesh of the given object and the information representative of the bone hierarchy of the given object, the generated compound collider being configured to move with the bone hierarchy of the given object.