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  • 1
    Online Resource
    Online Resource
    Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos
    Wiley ; 2019
    In:  Computer Graphics Forum Vol. 38, No. 2 ( 2019-05), p. 367-378
    Details
    In: Computer Graphics Forum, Wiley, Vol. 38, No. 2 ( 2019-05), p. 367-378
    Abstract: Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well‐accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi‐step dynamic human‐object interactions. Generating multi‐step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human‐object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state‐of‐the‐art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi‐step human‐object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.
    Type of Medium: Online Resource
    ISSN: 0167-7055 , 1467-8659
    URL: Issue
    URL: Article
    DOI: 10.1111/cgf.2019.38.issue-2
    DOI: 10.1111/cgf.13644
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1482655-0
    detail.hit.zdb_id: 246488-3
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  • 2
    Online Resource
    Online Resource
    Weatherscapes : nowcasting heat transfer and water continuity
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Graphics Vol. 40, No. 6 ( 2021-12), p. 1-19
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 40, No. 6 ( 2021-12), p. 1-19
    Abstract: Due to the complex interplay of various meteorological phenomena, simulating weather is a challenging and open research problem. In this contribution, we propose a novel physics-based model that enables simulating weather at interactive rates. By considering atmosphere and pedosphere we can define the hydrologic cycle - and consequently weather - in unprecedented detail. Specifically, our model captures different warm and cold clouds, such as mammatus, hole-punch, multi-layer, and cumulonimbus clouds as well as their dynamic transitions. We also model different precipitation types, such as rain, snow, and graupel by introducing a comprehensive microphysics scheme. The Wegener-Bergeron-Findeisen process is incorporated into our Kessler-type microphysics formulation covering ice crystal growth occurring in mixed-phase clouds. Moreover, we model the water run-off from the ground surface, the infiltration into the soil, and its subsequent evaporation back to the atmosphere. We account for daily temperature changes, as well as heat transfer between pedosphere and atmosphere leading to a complex feedback loop. Our framework enables us to interactively explore various complex weather phenomena. Our results are assessed visually and validated by simulating weatherscapes for various setups covering different precipitation events and environments, by showcasing the hydrologic cycle, and by reproducing common effects such as Foehn winds. We also provide quantitative evaluations creating high-precipitation cumulonimbus clouds by prescribing atmospheric conditions based on infrared satellite observations. With our model we can generate dynamic 3D scenes of weatherscapes with high visual fidelity and even nowcast real weather conditions as simulations by streaming weather data into our framework.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3478513.3480532
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 3
    Online Resource
    Online Resource
    Windy trees : computing stress response for developmental tree models
    Association for Computing Machinery (ACM) ; 2014
    In:  ACM Transactions on Graphics Vol. 33, No. 6 ( 2014-11-19), p. 1-11
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 33, No. 6 ( 2014-11-19), p. 1-11
    Abstract: We present a novel method for combining developmental tree models with turbulent wind fields. The tree geometry is created from internal growth functions of the developmental model and its response to external stress is induced by a physically-plausible wind field that is simulated by Smoothed Particle Hydrodynamics (SPH). Our tree models are dynamically evolving complex systems that (1) react in real-time to high-frequent changes of the wind simulation; and (2) adapt to long-term wind stress. We extend this process by wind-related effects such as branch breaking as well as bud abrasion and drying. In our interactive system the user can adjust the parameters of the growth model, modify wind properties and resulting forces, and define the tree's long-term response to wind. By using graphics hardware, our implementation runs at interactive rates for moderately large scenes composed of up to 20 tree models.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/2661229.2661252
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2014
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 4
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    Online Resource
    Interactive wood combustion for botanical tree models
    Association for Computing Machinery (ACM) ; 2017
    In:  ACM Transactions on Graphics Vol. 36, No. 6 ( 2017-12-31), p. 1-12
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 36, No. 6 ( 2017-12-31), p. 1-12
    Abstract: We present a novel method for the combustion of botanical tree models. Tree models are represented as connected particles for the branching structure and a polygonal surface mesh for the combustion. Each particle stores biological and physical attributes that drive the kinetic behavior of a plant and the exothermic reaction of the combustion. Coupled with realistic physics for rods, the particles enable dynamic branch motions. We model material properties, such as moisture and charring behavior, and associate them with individual particles. The combustion is efficiently processed in the surface domain of the tree model on a polygonal mesh. A user can dynamically interact with the model by initiating fires and by inducing stress on branches. The flames realistically propagate through the tree model by consuming the available resources. Our method runs at interactive rates and supports multiple tree instances in parallel. We demonstrate the effectiveness of our approach through numerous examples and evaluate its plausibility against the combustion of real wood samples.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3130800.3130814
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2017
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 5
    Online Resource
    Online Resource
    DCGrid : An Adaptive Grid Structure for Memory-Constrained Fluid Simulation on the GPU
    Association for Computing Machinery (ACM) ; 2022
    In:  Proceedings of the ACM on Computer Graphics and Interactive Techniques Vol. 5, No. 1 ( 2022-05-04), p. 1-14
    Details
    In: Proceedings of the ACM on Computer Graphics and Interactive Techniques, Association for Computing Machinery (ACM), Vol. 5, No. 1 ( 2022-05-04), p. 1-14
    Abstract: We introduce Dynamic Constrained Grid (DCGrid), a hierarchical and adaptive grid structure for fluid simulation combined with a scheme for effectively managing the grid adaptations. DCGrid is designed to be implemented on the GPU and used in high-performance simulations. Specifically, it allows us to efficiently vary and adjust the grid resolution across the spatial domain and to rapidly evaluate local stencils and individual cells in a GPU implementation. A special feature of DCGrid is that the control of the grid adaption is modeled as an optimization under a constraint on the maximum available memory, which addresses the memory limitations in GPU-based simulation. To further advance the use of DCGrid in high-performance simulations, we complement DCGrid with an efficient scheme for approximating collisions between fluids and static solids on cells with different resolutions. We demonstrate the effectiveness of DCGrid for smoke flows and complex cloud simulations in which terrain-atmosphere interaction requires working with cells of varying resolution and rapidly changing conditions. Finally, we compare the performance of DCGrid to that of alternative adaptive grid structures.
    Type of Medium: Online Resource
    ISSN: 2577-6193
    URL: Article
    DOI: 10.1145/3522608
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2964069-6
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  • 6
    Online Resource
    Online Resource
    Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2019
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, No. 01 ( 2019-07-17), p. 8001-8008
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33, No. 01 ( 2019-07-17), p. 8001-8008
    Abstract: Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google.com/view/struct2depth.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v33i01.33018001
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2019
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  • 7
    Online Resource
    Online Resource
    Fire in paradise : mesoscale simulation of wildfires
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Graphics Vol. 40, No. 4 ( 2021-08-31), p. 1-15
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 40, No. 4 ( 2021-08-31), p. 1-15
    Abstract: Resulting from changing climatic conditions, wildfires have become an existential threat across various countries around the world. The complex dynamics paired with their often rapid progression renders wildfires an often disastrous natural phenomenon that is difficult to predict and to counteract. In this paper we present a novel method for simulating wildfires with the goal to realistically capture the combustion process of individual trees and the resulting propagation of fires at the scale of forests. We rely on a state-of-the-art modeling approach for large-scale ecosystems that enables us to represent each plant as a detailed 3D geometric model. We introduce a novel mathematical formulation for the combustion process of plants - also considering effects such as heat transfer, char insulation, and mass loss - as well as for the propagation of fire through the entire ecosystem. Compared to other wildfire simulations which employ geometric representations of plants such as cones or cylinders, our detailed 3D tree models enable us to simulate the interplay of geometric variations of branching structures and the dynamics of fire and wood combustion. Our simulation runs at interactive rates and thereby provides a convenient way to explore different conditions that affect wildfires, ranging from terrain elevation profiles and ecosystem compositions to various measures against wildfires, such as cutting down trees as firebreaks, the application of fire retardant, or the simulation of rain.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3450626.3459954
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 8
    Online Resource
    Online Resource
    Learning to reconstruct botanical trees from single images
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Graphics Vol. 40, No. 6 ( 2021-12), p. 1-15
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 40, No. 6 ( 2021-12), p. 1-15
    Abstract: We introduce a novel method for reconstructing the 3D geometry of botanical trees from single photographs. Faithfully reconstructing a tree from single-view sensor data is a challenging and open problem because many possible 3D trees exist that fit the tree's shape observed from a single view. We address this challenge by defining a reconstruction pipeline based on three neural networks. The networks simultaneously mask out trees in input photographs, identify a tree's species, and obtain its 3D radial bounding volume - our novel 3D representation for botanical trees. Radial bounding volumes (RBV) are used to orchestrate a procedural model primed on learned parameters to grow a tree that matches the main branching structure and the overall shape of the captured tree. While the RBV allows us to faithfully reconstruct the main branching structure, we use the procedural model's morphological constraints to generate realistic branching for the tree crown. This constraints the number of solutions of tree models for a given photograph of a tree. We show that our method reconstructs various tree species even when the trees are captured in front of complex backgrounds. Moreover, although our neural networks have been trained on synthetic data with data augmentation, we show that our pipeline performs well for real tree photographs. We evaluate the reconstructed geometries with several metrics, including leaf area index and maximum radial tree distances.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3478513.3480525
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 9
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    Online Resource
    Procedural Urban Forestry
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Graphics Vol. 41, No. 2 ( 2022-04-30), p. 1-18
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 41, No. 2 ( 2022-04-30), p. 1-18
    Abstract: The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3502220
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 10
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    Online Resource
    Language-driven synthesis of 3D scenes from scene databases
    Association for Computing Machinery (ACM) ; 2018
    In:  ACM Transactions on Graphics Vol. 37, No. 6 ( 2018-12-31), p. 1-16
    Details
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 37, No. 6 ( 2018-12-31), p. 1-16
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    URL: Article
    DOI: 10.1145/3272127.3275035
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2018
    detail.hit.zdb_id: 2006336-2
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