641 research outputs found
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Under construction: infrastructure and modern fiction
In this dissertation, I argue that infrastructural development, with its technological promises but widening geographic disparities and social and environmental consequences, informs both the narrative content and aesthetic forms of modernist and contemporary Anglophone fiction. Despite its prevalent material forms—roads, rails, pipes, and wires—infrastructure poses particular formal and narrative problems, often receding into the background as mere setting. To address how literary fiction theorizes the experience of infrastructure requires reading “infrastructurally”: that is, paying attention to the seemingly mundane interactions between characters and their built environments. The writers central to this project—James Joyce, William Faulkner, Karen Tei Yamashita, and Mohsin Hamid—take up the representational challenges posed by infrastructure by bringing transit networks, sanitation systems, and electrical grids and the histories of their development and use into the foreground. These writers call attention to the political dimensions of built environments, revealing the ways infrastructures produce, reinforce, and perpetuate racial and socioeconomic fault lines. They also attempt to formalize the material relations of power inscribed by and within infrastructure; the novel itself becomes an imaginary counterpart to the technologies of infrastructure, a form that shapes and constrains what types of social action and affiliation are possible
InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
This paper addresses a novel task of anticipating 3D human-object
interactions (HOIs). Most existing research on HOI synthesis lacks
comprehensive whole-body interactions with dynamic objects, e.g., often limited
to manipulating small or static objects. Our task is significantly more
challenging, as it requires modeling dynamic objects with various shapes,
capturing whole-body motion, and ensuring physically valid interactions. To
this end, we propose InterDiff, a framework comprising two key steps: (i)
interaction diffusion, where we leverage a diffusion model to encode the
distribution of future human-object interactions; (ii) interaction correction,
where we introduce a physics-informed predictor to correct denoised HOIs in a
diffusion step. Our key insight is to inject prior knowledge that the
interactions under reference with respect to contact points follow a simple
pattern and are easily predictable. Experiments on multiple human-object
interaction datasets demonstrate the effectiveness of our method for this task,
capable of producing realistic, vivid, and remarkably long-term 3D HOI
predictions.Comment: ICCV 2023; Project Page: https://sirui-xu.github.io/InterDiff
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
Gestures that accompany speech are an essential part of natural and efficient
embodied human communication. The automatic generation of such co-speech
gestures is a long-standing problem in computer animation and is considered an
enabling technology in film, games, virtual social spaces, and for interaction
with social robots. The problem is made challenging by the idiosyncratic and
non-periodic nature of human co-speech gesture motion, and by the great
diversity of communicative functions that gestures encompass. Gesture
generation has seen surging interest recently, owing to the emergence of more
and larger datasets of human gesture motion, combined with strides in
deep-learning-based generative models, that benefit from the growing
availability of data. This review article summarizes co-speech gesture
generation research, with a particular focus on deep generative models. First,
we articulate the theory describing human gesticulation and how it complements
speech. Next, we briefly discuss rule-based and classical statistical gesture
synthesis, before delving into deep learning approaches. We employ the choice
of input modalities as an organizing principle, examining systems that generate
gestures from audio, text, and non-linguistic input. We also chronicle the
evolution of the related training data sets in terms of size, diversity, motion
quality, and collection method. Finally, we identify key research challenges in
gesture generation, including data availability and quality; producing
human-like motion; grounding the gesture in the co-occurring speech in
interaction with other speakers, and in the environment; performing gesture
evaluation; and integration of gesture synthesis into applications. We
highlight recent approaches to tackling the various key challenges, as well as
the limitations of these approaches, and point toward areas of future
development.Comment: Accepted for EUROGRAPHICS 202
University of Windsor Graduate Calendar 2023 Spring
https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp
Northeastern Illinois University, Academic Catalog 2023-2024
https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp
University of Windsor Graduate Calendar 2023 Winter
https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp
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