Abstract: A method of generating a three-dimensional (3D) model includes obtaining a set of two-dimensional (2D) images of a scene acquired by one or more cameras from a set of camera angles and camera positions, obtaining the set of camera angles and camera positions based on obtaining, for each 2D image in the set of 2D images, a respective camera angle and a respective camera position for the 2D image, training a neural radiance field (NeRF) model, using the set of 2D images and the set of camera angles and camera positions as a training dataset, to obtain a trained NeRF model, generating a set of 2D depth maps based on the trained NeRF model, and generating a 3D polygonal mesh representing at least one object of one or more objects in the scene based on the set of 2D depth maps.

Abstract: A method of generating a three-dimensional (3D) model includes obtaining a set of two-dimensional (2D) images of a scene acquired by one or more cameras from a plurality of camera angles at a plurality of camera positions. Each 2D image corresponds to a respective camera angle and a respective camera position. The method further includes obtaining the respective camera angle and the respective camera position for each 2D image, and generating one or more semantic masks from the set of 2D images. Each semantic mask corresponds to a class of one or more objects in the scene. The method further includes training a neural radiance field (NeRF) model, using the set of 2D images and the one or more semantic masks as a training dataset, to obtain a trained NeRF model. The trained NeRF model is an implicit 3D model of the one or more objects in the scene.

Abstract: Implementations disclosed herein are directed to using a machine learning model to generate three-dimensional models of objects. In some implementations, a computer implemented method can include the steps of: generating, by a machine-learning model, a three-dimensional model of an object from a three-dimensional model of an initial object and conditional input(s) specifying target properties of the generated object; generating two-dimensional image(s) of the generated object from the three-dimensional model of the generated object; generating a respective image embedding for each of the two-dimensional image(s) of the generated object using an image embedding model; generating a respective conditional embedding for each of the conditional input(s); and updating parameters of the machine-learning model based on a comparison of the respective conditional embedding for each of the conditional input(s) and the respective image embeddings for each of the two-dimensional image(s) of the generated object.

Abstract: A method of generating a three-dimensional (3D) model includes obtaining a set of two-dimensional (2D) images of a scene acquired by one or more cameras from a set of camera angles and camera positions, obtaining the set of camera angles and camera positions based on obtaining, for each 2D image in the set of 2D images, a respective camera angle and a respective camera position for the 2D image, training a neural radiance field (NeRF) model, using the set of 2D images and the set of camera angles and camera positions as a training dataset, to obtain a trained NeRF model, generating a set of 2D depth maps based on the trained NeRF model, and generating a 3D polygonal mesh representing at least one object of one or more objects in the scene based on the set of 2D depth maps.

Abstract: A video game system and method analyze virtual contact between an avatar and a virtual object within a video game. The point of contact of the virtual contact on the virtual object and/or the intensity of contact of the virtual contact may then be used to determine a subsequent virtual action to be performed within the video game. The virtual action, with any virtual movement thereof, may be carried out in a realistic manner within the video game by determining a virtual trajectory of the motion. The virtual trajectory may be determined using a motion model. The motion model may provide the virtual trajectory of the virtual object based at least in part on one or more parameters of the virtual object, such as a weight parameter. The motion model may be trained using training video clips with realistic motion of virtual objects.

Abstract: A method of generating a three-dimensional (3D) model includes obtaining a set of two-dimensional (2D) images of a scene acquired by one or more cameras from a plurality of camera angles at a plurality of camera positions. Each 2D image corresponds to a respective camera angle and a respective camera position. The method further includes obtaining the respective camera angle and the respective camera position for each 2D image, and generating one or more semantic masks from the set of 2D images. Each semantic mask corresponds to a class of one or more objects in the scene. The method further includes training a neural radiance field (NeRF) model, using the set of 2D images and the one or more semantic masks as a training dataset, to obtain a trained NeRF model. The trained NeRF model is an implicit 3D model of the one or more objects in the scene.

Abstract: A video game system and method analyze virtual contact between an avatar and a virtual object within a video game. The point of contact of the virtual contact on the virtual object and/or the intensity of contact of the virtual contact may then be used to determine a subsequent virtual action to be performed within the video game. The virtual action, with any virtual movement thereof, may be carried out in a realistic manner within the video game by determining a virtual trajectory of the motion. The virtual trajectory may be determined using a motion model. The motion model may provide the virtual trajectory of the virtual object based at least in part on one or more parameters of the virtual object, such as a weight parameter. The motion model may be trained using training video clips with realistic motion of virtual objects.

Abstract: An article support device has a pair of support platforms arranged symmetrically and opposite to each other. Each of the support platforms includes a support frame, a support plate disposed on a top of the support frame and having a horizontal support top surface, and at least one positioning block installed on the horizontal support top surface. The horizontal support top surfaces of the support plates are located at a same height position. The article is supported on the horizontal support top surfaces and positioned between the positioning blocks on the support plates. An accommodation space is defined between the support plates and the support frames, and an automated guided vehicle moves into the accommodation space. An article carried on the article support device is loadable onto the automatic guided vehicle or the article carried on the automatic guided vehicle is unloadable onto the article support device.

Abstract: An article support device has a pair of support platforms arranged symmetrically and opposite to each other. Each of the support platforms includes a support frame, a support plate disposed on a top of the support frame and having a horizontal support top surface, and at least one positioning block installed on the horizontal support top surface. The horizontal support top surfaces of the support plates are located at a same height position. The article is supported on the horizontal support top surfaces and positioned between the positioning blocks on the support plates. An accommodation space is defined between the support plates and the support frames, and an automated guided vehicle moves into the accommodation space. An article carried on the article support device is loadable onto the automatic guided vehicle or the article carried on the automatic guided vehicle is unloadable onto the article support device.