Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.
We introduce a novel framework for using natural language to generate and edit 3D indoor scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3D scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the sub-scene level, acting on groups of objects. We learn how to manipulate these sub-scenes by analyzing existing 3D scenes. We perform edits by first parsing a natural language command from the user and transforming it into a semantic scene graph that is used to retrieve corresponding sub-scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3D scene is synthesized by aligning the augmented sub-scene with the user's current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made scenes and find that our method significantly outperforms prior work and is comparable to handmade scenes even when complex and varied natural sentences are used.
Figure 1: Different interaction landscapes representing the interactions of a motion driver with a static object. We capture the motion trajectories (red) and encode their signatures into a descriptor that can be used for comparing interactions. From left to right: a cloth simulation interacting with a support structure, a human model walking on a floor, a wind simulation interacting with a car, and a robotic hand grasping a cup. AbstractInteractions play a key role in understanding objects and scenes, for both virtual and real world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape, capturing its observed use in a spatio-temporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.
Figure 1: The static tree model on the left is converted into a developmental model (middle part) that encompasses the ability to create arbitrary intermediate stages between a very young model and the given geometry. We define a "growth space" that allows the user to edit the model in an enhanced way. A corresponding model is shown on the right. AbstractGiven a static tree model we present a method to compute developmental stages that approximate the tree's natural growth. The tree model is analyzed and a graph-based description its skeleton is determined. Based on structural similarity, branches are added where pruning has been applied or branches have died off over time. Botanic growth models and allometric rules enable us to produce convincing animations from a young tree that converge to the given model. Furthermore, the user can explore all intermediate stages.By selectively applying the process to parts of the tree even complex models can be edited easily. This form of reverse engineering enables users to create rich natural scenes from a small number of static tree models.
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