An intuitive object-generation experience is provided by employing an autoencoder neural network to reduce the dimensionality of a procedural model. A set of sample objects are generated using the procedural model. In embodiments, the sample objects may be selected according to visual features such that the sample objects are uniformly distributed in visual appearance. Both procedural model parameters and visual features from the sample objects are used to train an autoencoder neural network, which maps a small number of new parameters to the larger number of procedural model parameters of the original procedural model. A user interface may be provided that allows users to generate new objects by adjusting the new parameters of the trained autoencoder neural network, which outputs procedural model parameters. The output procedural model parameters may be provided to the procedural model to generate the new objects.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: generating a set of sample objects using a procedural model; providing procedural model parameters and visual features from the sample objects as training data; and training an autoencoder neural network using the training data, the autoencoder neural network being usable to generate new objects. 2. The one or more computer storage media of claim 1, wherein the set of sample objects is generated by: generating a categorization tree using the procedural model to generate objects to populate the categorization tree; sampling the categorization tree to obtain objects uniformly distributed with respect to visual appearance; and iteratively adding additional objects to the categorization tree, computing a new categorization tree, and sampling the new categorization tree until a criterion is satisfied. 3. The one or more computer storage media of claim 2, wherein sampling the categorization tree employs a probability distribution based on individual selection probabilities computed for each leaf node of the categorization tree as follows: P i = ( d i × s i ) - 1 Σ i  ( d i × s i ) - 1 wherein di is a depth of a node i starting from a root node and si is a number of siblings. 4. The one or more computer storage media of claim 2, wherein the criterion is: Et−Et-1<w(Et-1−Et-2) wherein Et is potential energy in the categorization tree at iteration t, and 0<w<1 is a weighting constant, wherein potential energy is computed as: E = 1  S   ∑ i ∈ s   e i wherein S is a set of objects in the categorization tree and ei is a potential energy of object i defined as: ei=min|xi−xj| i,jεS,i≠j wherein xi and xj are visual features of objects i and j respectively. 5. The one or more computer storage media of claim 1, wherein the visual features from the sample objects are based on shape. 6. The one or more computer storage media of claim 5, wherein the sample objects are 3D objects and the visual feature for each sample object is generated by: computing a plurality of 2D renders of the sample object; extracting a silhouette-based histogram from each 2D render; and concatenating the silhouette-based histograms to form a feature vector for the sample object. 7. The one or more computer storage media of claim 5, wherein the sample objects are 2D objects and the visual feature for each sample object comprises a feature vector based on a texton histogram of a shape of the sample object. 8. The one or more computer storage media of claim 1, wherein the autoencoder neural network comprises a five hidden layer symmetric autoencoder neural network. 9. The one or more computer storage media of claim 1, wherein the operations further comprise generating a user interface that exposes a set of new parameters based on the autoencoder neural network, the set of new parameters being adjustable via user interface elements to generate new objects. 10. The one or more computer storage media of claim 9, wherein a decoding part of the autoencoder neural network is employed to generate new objects, and wherein the new parameters adjustable via the user interface elements correspond with nodes at a bottleneck layer of the autoencoder neural network. 11. A computerized method comprising: providing a procedural model having a set of procedural model parameters; generating a set of sample objects using the procedural model; providing procedural model parameters and visual features from the sample objects as training data; training an autoencoder neural network using the training data, the autoencoder neural network providing a set of new parameters, the new parameters being fewer in number than the procedural model parameters; and generating a user interface that exposes the set of new parameters via user interface elements allowing a user to adjust at least a portion of the new parameters via the user interface elements to generate new objects. 12. The method of claim 11, wherein the set of sample objects is generated by: generating a categorization tree using the procedural model to generate objects to populate the categorization tree; sampling the categorization tree to obtain objects uniformly distributed with respect to visual appearance; and iteratively adding additional objects to the categorization tree, computing a new categorization tree, and sampling the new categorization tree until a criterion is satisfied. 13. The method of claim 12, wherein sampling the categorization tree employs a probability distribution based on individual selection probabilities computed for each leaf node of the categorization tree as follows: P i = ( d i × s i ) - 1 Σ i  ( d i × s i ) - 1 wherein di is a depth of a node i starting from a root node and si is a number of siblings. 14. The method of claim 12, wherein the criterion is: Et−Et-1<w(Et-1−Et-2) wherein Et is potential energy in the categorization tree at iteration t, and 0<w<1 is a weighting constant, wherein potential energy is computed as: E = 1  S   ∑ i ∈ s   e i wherein S is a set of objects in the categorization tree and ei is a potential energy of object i defined as: ei=min|xi−xj| i,jεS,i≠j wherein xi and xj are visual features of objects i and j respectively. 15. The method of claim 11, wherein the visual features from the sample objects are based on shape. 16. The method of claim 15, wherein the sample objects are 3D objects and the visual feature for each sample object is generated by: computing a plurality of 2D renders of the sample object; extracting a silhouette-based histogram from each 2D render; and concatenating the silhouette-based histograms to form a feature vector for the sample object. 17. The method of claim 15, wherein the sample objects are 2D objects and the visual feature for each sample object comprises a feature vector based on a texton histogram of a shape of the sample object. 18. The method of claim 11, wherein the autoencoder neural network comprises a five hidden layer symmetric autoencoder neural network. 19. The method of claim 11, wherein the method further comprises: receiving input via the user interface adjusting the new parameters to provide adjusted new parameters; providing the adjusted new parameters to the autoencoder neural network to generate output procedural model parameters; providing the output procedural model parameters to the procedural model to generate an output object. 20. A computerized method comprising: generating a set of sample objects using a procedural model by: generating a categorization tree using the procedural model to generate objects to populate the categorization tree, sampling the categorization tree to obtain objects uniformly distributed with respect to visual appearance, and iteratively adding additional objects to the categorization tree, computing a new categorization tree, and sampling the new categorization tree until a criterion is satisfied; training an autoencoder neural network using procedural model parameters and visual features from the sample objects as training data; and generating a user interface that exposes a set of new parameters from the autoencoder neural network, the user interface including user interface elements allowing a user to adjust at least a portion of the new parameters to generate new objects.