Abstract: A method and system provide the ability to extend immutable data. A first version of immutable design data is created. The first version of immutable design data is edited to create a second version of immutable design data. Subsequent to the creation of the second version of immutable data, the first version of immutable design data is augmented with first extendable data without creating a new version of immutable data.

Abstract: A method of modifying metadata associated with a media file is described the method comprising receiving a first media file; receiving a first metadata associated with the first media file; receiving an identifier of a second media file; and modifying the first metadata to include the identifier of the second media file.

Abstract: A method and system provide for annotating a computer-aided design (CAD) drawing. Existing drawings are obtained and include annotations and geometries that serve as hosts. A machine learning (ML) model is trained on the extracted geometries and annotations. A new drawing is obtained. First user input selecting a first geometry in the new drawing is received and the first annotation is created. The ML model generates potential new hosts and annotations. The potential new annotations are displayed in the new drawing and second user input selects one of the potential new annotations to utilize as one or more new annotations.

Abstract: A method and system provide the ability to interpolate fluids. At least two keyframes are produced, for a physics based fluid simulation. The keyframes are within a continuous-time framework and separated by a defined interval. Each keyframe includes one or more fluid elements having a corresponding state. Data is prepared utilizing a pre-trained transformer-based network by: (i) handling a tokenization process in a physics-adapted context; and (ii) generating temporal embeddings for states of the one or more fluid elements. Based on the prepared data, a time-continuous density is prepared for substeps between the two keyframes using a density network.

Abstract: In various embodiments a computer-implemented method for providing sustainability insights to a user designing an object. The method includes determining a value of a sustainability metric associated with a design of an object; determining a target value for the sustainability metric; determining an amount of progress made towards achieving the target value for the sustainability metric based on the value of the sustainability metric and the target value for the sustainability metric; and displaying, via a graphical user interface (GUI), a visual indication of the amount of progress made towards achieving the target value for the sustainability metric.

Abstract: In various embodiments, a knowledge capture application automatically captures knowledge associated with computer-based tasks. While a computer-based task is performed during a computer-based session, the knowledge capture application performs operation(s) on a user utterance to automatically generate a knowledge item. The knowledge capture application performs classification operation(s) on the knowledge item to generate a categorized knowledge item that associates the knowledge item with a first category. Subsequently, the knowledge capture application modifies a first element included in a graphical user interface to visually indicate an actual capture level associated with the first category. The knowledge capture application generates a knowledge dataset based on the first categorized knowledge item for storage or display.

Abstract: A method and system provide for augmenting a photograph. An unlabeled photograph is obtained. A weakly augmented photograph and a strongly augmented photograph are obtained from the unlabeled photograph based on different types of data augmentation methods. The weakly augmented photograph is processed through a model to generate multiple weakly augmented photograph class predictions (with assigned probabilities). The multiple weakly augmented photograph class predictions are converted into positive pseudo-labels (indicating a presence of a class) or negative pseudo-labels (indicating absence of a class) using different fixed percentile thresholds. The strongly augmented photograph is processed through the model to generate a strongly augmented photograph class prediction. The model is trained to make the strongly augmented photograph label prediction match the positive pseudo-label via a cross-entropy loss. The trained model is then utilized to label the unlabeled photograph with multiple labels.

Abstract: In various embodiments, a training application trains machine learning models to perform tasks associated with 3D CAD objects that are represented using B-reps. In operation, the training application computes a preliminary result via a machine learning model based on a representation of a 3D CAD object that includes a graph and multiple 2D UV-grids. Based on the preliminary result, the training application performs one or more operations to determine that the machine learning model has not been trained to perform a first task. The training application updates at least one parameter of a graph neural network included in the machine learning model based on the preliminary result to generate a modified machine learning model. The training application performs one or more operations to determine that the modified machine learning model has been trained to perform the first task.

Abstract: A computer-implemented method for generating a virtual collection of digital materials includes: generating a virtual three-dimensional (3D) design workspace; and, in response to a first operation that is associated with a first media file: automatically generating a virtual 3D representation of the first media file; and automatically incorporating the virtual 3D representation of the first media file into the virtual 3D design workspace.

Abstract: In various embodiments, a style comparison application compares geometric styles of different three dimensional (3D) computer-aided design (CAD) objects. In operation, the style comparison application executes a trained neural network one or more times to map 3D CAD objects to feature map sets. The style comparison application computes a first set of style signals based on a first feature set included in the feature map sets. The style comparison application computes a second set of style signals based on a second feature set included in the feature map sets. Based on the first set of style signals and the second set of style signals, the style comparison application determines a value for a style comparison metric. The value for the style comparison metric quantifies a similarity or a dissimilarity in geometric style between a first 3D CAD object and a second 3D CAD object.

Abstract: Techniques are disclosed for posing three-dimensional (3D) objects in extended reality (XR) environments using multiple hands. In some embodiments, an animation application selects a posing mode based on user interactions and adjusts joints and/or bones associated with a 3D object based on the posing mode and the user interactions. The posing mode can be a symmetric bimanual posing mode in which two joints are selected and adjusted simultaneously, an asymmetric bimanual posing mode a reference joint is selected with one hand to serve as a pivot while a second joint is adjusted by another hand, a virtual hand posing mode in which virtual hands are created to fix joints in place, or a bone manipulation mode in which bones can be selected and adjusted, with adjustments to the bones influencing joints that are connected to the bones.

Abstract: A method and system provide for operating a three-dimensional (3D) computer animation and visual effects application (3D application). Execution of a multi-step 3D animation, modeling, or visual effects operation is initialized. Progression of the operation is controlled via a node-based state machine having a plurality of stage nodes daisy-chained via defined dependencies, each stage node including a condition attribute. Upon activation of a first stage node, a script associated with the first stage node that configures behavior of the 3D application is activated. Application events are monitored. A determination is made that the condition attribute of the first stage node has been satisfied. Upon satisfaction of the condition attribute, execution transitions to a subsequent stage node and a corresponding script is executed that modifies a scene state or animation state.

Abstract: Techniques are disclosed for controlling robotic systems to perform assembly tasks. In some embodiments, a robot control application receives sensor data associated with one or more parts. The robot control application applies a grasp perception model to predict one or more grasp proposals indicating regions of the one or more parts that a robotic system can grasp. The robot control application causes the robotic system to grasp one of the parts based on a corresponding grasp proposal. If the pose of the grasped part needs to be changed in order to assemble the part with one or more other parts, the robot control application determines movements of the robotic system required to re-grasp the part in a different pose. In addition, the robot control application determines movements of the robot system for assembling the part with the one or more other parts based on results of a motion planning technique.

Abstract: A method and system provide for automating drawing. A drawing of two or more entities is obtained and a resolution is determined. Based on the resolution, the drawing is quantized into a cell map in which spatial information is lost. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data and domain specific information. The cell map is utilized to automate a drawing process workflow faster and more efficiently than relying on conventional geometry data structures.

Abstract: A method, apparatus, and system provide the ability to perform a drop test using a response surface. Inputs including a target safety factor (T-SF), a drop height, and a computer model, are acquired. An initial template is evaluated by computing a safety factor for a set of orientations, for each model point MP in the computer model across a set of times (t). A minimum safety factor is determined and a response surface for the model is generated. An actual safety factor SFm is generated by conducting a drop test simulation of the model based on a point having the minimum safety factor and a corresponding orientation. Consistency/validity of the actual safety factor is compared to the target safety factor and the model either passes or additional points may be added to the set of points and the process repeats based on an updated response surface.

Abstract: A method and system provide for computer aided drawing (CAD) automation. A CAD drawing in a first format that is native to a CAD application is obtained. Markup for the CAD drawing that is in a second non-native format is acquired. A machine learning engine processes the markup using a machine learning model, to identify a markup type, identify markup properties, correlate, the markup to a CAD object needed to update the CAD drawing, and determine, CAD object properties and CAD application properties. The CAD object with the CAD object properties is provided in the CAD application using the CAD application properties. User input is accepted and the CAD drawing is updated based thereon. The machine learning model is updated based on the user input and utilized to process future markups.

Abstract: A method and system provide the ability to generate and use synthetic data to extract elements from a floor plan drawing. A room layout is generated. Room descriptions are used to generate and place synthetic instances of symbol elements in each room. A floor plan drawing is obtained and pre-processed to determine a drawing area. Based on the synthetic data symbols in the floor plan drawing are detected. Based on the detected symbols, building information model (BIM) elements are fetched and placed in the floor plan drawing.

Abstract: A computer-implemented method and system provide for insights/recommendations to a user of a software product. A computer application autonomously determines a count of distinct commands executed by a user. Based on the count, the computer application autonomously determines the insight that includes a feature or new distinct command. The computer application then autonomously recommends the insight to the user.

Abstract: A technique for generating designs includes: causing one or more candidate designs to be displayed within a virtual-reality (VR) environment; receiving a user input associated with a first candidate design included in the one or more candidate designs via the VR environment; generating a modified design based at least on the user input and the first candidate design; and generating a plurality of output designs via a generative design process based on the modified design.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a B-Rep model including parametric surfaces and associated coedges; generating feature matrices from the coedges associated with the parametric surfaces; using the feature matrices in a convolution layer of a convolutional neural network of a machine learning algorithm to recognize a collection of faces or edges in the B-Rep model, including concatenating results of transforming the feature matrices using a topological walk matrix that represents topological information for the parametric surfaces based on adjacency relationships among the coedges (e.g.