Semantic Graph Convolutional Networks for 3D Human Pose Regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code can be found at https://github.com/garyzhao/SemGC

An Event-Centric Planning Approach for Dynamic Real-Time Narrative

In this paper, we propose an event-centric planning framework for directing interactive narratives in complex 3D environments populated by virtual humans. Events facilitate precise authorial control over complex interactions involving groups of actors and objects, while planning allows the simulation of causally consistent character actions that conform to an overarching global narrative. Events are defined by preconditions, postconditions, costs, and a centralized behavior structure that simultaneously manages multiple participating actors and objects. By planning in the space of events rather than in the space of individual character capabilities, we allow virtual actors to exhibit a rich repertoire of individual actions without causing combinatorial growth in the planning branching factor. Our system produces long, cohesive narratives at interactive rates, allowing a user to take part in a dynamic story that, despite intervention, conforms to an authored structure and accomplishes a predetermined goal

SPREAD: Sound Propagation and Perception for Autonomous Agents in Dynamic Environments

The perception of sensory information and its impact on behavior is a fundamental component of being human. While visual perception is considered for navigation, collision, and behavior selection, the acoustic domain is relatively unexplored. Recent work in acoustics focuses on synthesizing sound in 3D environments; however, the perception of acoustic signals by a virtual agent is a useful and realistic adjunct to any behavior selection mechanism. In this paper, we present SPREAD, a novel agent-based sound perception model using a discretized sound packet representation with acoustic features including amplitude, frequency range, and duration. SPREAD simulates how sound packets are propagated, attenuated, and degraded as they traverse the virtual environment. Agents perceive and classify the sounds based on the locally-received packet set using a hierarchical clustering scheme, and have individualized hearing and understanding of their surroundings. Using this model, we demonstrate several simulations that greatly enrich controls and outcomes

Improved Benchmarking for Steering Algorithms

The statistical analysis of multi-agent simulations requires a definitive set of benchmarks that represent the wide spectrum of challenging scenarios that agents encounter in dynamic environments, and a scoring method to objectively quantify the performance of a steering algorithm for a particular scenario. In this paper, we first recognize several limitations in prior evaluation methods. Next, we define a measure of normalized effort that penalizes deviation from desired speed, optimal paths, and collisions in a single metric. Finally, we propose a new set of benchmark categories that capture the different situations that agents encounter in dynamic environments and identify truly challenging scenarios for each category. We use our method to objectively evaluate and compare three state of the art steering approaches and one baseline reactive approach. Our proposed scoring mechanism can be used (a) to evaluate a single algorithm on a single scenario, (b) to compare the performance of an algorithm over different benchmarks, and (c) to compare different steering algorithms

A Behavior Authoring Framework for Multi-Actor Simulations

There has been growing academic and industry interest in the behavioral animation of autonomous actors in virtual worlds. However, it remains a considerable challenge to author complicated interactions between multiple actors in a way that balances automation and control flexibility. In this paper, we propose a behavior authoring framework which provides the user with complete control over the domain of the system: the state space, action space and cost of executing actions. Actors are specialized using effect and cost modifiers, which modify existing action definitions, and constraints, which prune action choices in a state-dependent manner. Behaviors are used to define goals and objective functions for an actor. Actors having common or conflicting goals are grouped together to form a composite domain, and a multi-agent planner is used to generate complicated interactions between multiple actors. We demonstrate the effectiveness of our framework by authoring and generating a city simulation involving multiple pedestrians and vehicles that interact with one another to produce complex multi-actor behaviors

GPU-based dynamic search on adaptive resolution grids

Abstract — This paper presents a GPU-based wave-front propagation technique for multi-agent path planning in ex-tremely large, complex, dynamic environments. Our work proposes an adaptive subdivision of the environment with efficient indexing, update, and neighbor-finding operations on the GPU to address several known limitations in prior work. In particular, an adaptive environment representation reduces the device memory requirements by an order of magnitude which enables for the first time, GPU-based goal path planning in truly large-scale environments (> 2048 m2) for hundreds of agents with different targets. We compare our approach to prior work that uses an uniform grid on several challenging navigation benchmarks and report significant memory savings, and up to a 1000X computational speedup. I