Method and a system for generating 3d scenes

A server for volume rendering of 3D scenes, the server comprising at least one processor and at least one non-transitory memory storing executable instructions, which, when executed by the at least one processor, cause the server to train a given machine-learning algorithm (MLA) of a plurality of MLAs to identify a boundary between a plurality of interpenetrated objects to be rendered in a given 3D scene, by:

• receiving, from a given camera of a plurality of cameras, a respective sequence of training 2D images representative of a plurality of interpenetrated training objects;
• generating, using respective sequences of training 2D images from the plurality of cameras, a sequence of rectified training 2D images;
• generating, for a given training object of the plurality of interpenetrated training objects in a given rectified training 2D image of the sequence of rectified training 2D images, a respective Skinned-Multi Person Linear Model (SMPL) pose estimate, the respective SMPL pose estimate including:
• pose vertices defining a surface of the given training object in a respective pose thereof in the given rectified training 2D image;
• and
• a respective SMPL pose vector comprising values of a plurality of SMPL pose parameters representative of the respective pose of the given training object in the given rectified training 2D image;
• retrieving, for the given training object of the plurality of interpenetrated training objects, a canonical SMPL pose, the canonical SMPL pose including canonical vertices defining the surface of the given training object in a predetermined pose thereof;
• generating a closed 3D space around the respective SMPL pose estimates associated with the plurality of interpenetrated training objects;
• generating, along an inner surface of the closed 3D space, a plurality of viewpoints such that each one of the plurality of viewpoints is directed to a center of the closed 3D space;
• extending, from each viewpoint of the plurality of viewpoints, a respective plurality of rays through the respective SMPL pose estimates of the plurality of interpenetrated training objects;
• for a given point along a given ray, identifying a corresponding canonical point for the canonical SMPL pose associated with each one of the plurality of interpenetrated training objects;
• generating, for the given MLA of the plurality of MLAs, a respective training set of data,
• the respective training set of data including a plurality of training digital objects, a given one of which, for the given point along the given ray comprises:
• (i) coordinates of the corresponding canonical point associated with the respective training object;
• and (ii) the respective SMPL pose vector associated with the respective training object;
• using respective training sets of data, jointly training the plurality of MLAs to identify the boundary of the respective object of the plurality of interpenetrated objects, by:
• feeding respective training digital objects associated with the given point to each MLA of the plurality of MLAs, thereby causing each one of the plurality of MLAs to generate a respective predicted signed distance field (SDF)value for the corresponding canonical point;
• and
• applying an SDF loss function, the SDF loss function being configured to penalize the respective predicted SDF value of the given point generated by the given MLA in response to the respective predicted SDF value generated by the given MLA being equal to the respective predicted SDF value generated by an other MLA of the plurality of MLAs.