2,077 research outputs found
Robust Motion In-betweening
In this work we present a novel, robust transition generation technique that
can serve as a new tool for 3D animators, based on adversarial recurrent neural
networks. The system synthesizes high-quality motions that use
temporally-sparse keyframes as animation constraints. This is reminiscent of
the job of in-betweening in traditional animation pipelines, in which an
animator draws motion frames between provided keyframes. We first show that a
state-of-the-art motion prediction model cannot be easily converted into a
robust transition generator when only adding conditioning information about
future keyframes. To solve this problem, we then propose two novel additive
embedding modifiers that are applied at each timestep to latent representations
encoded inside the network's architecture. One modifier is a time-to-arrival
embedding that allows variations of the transition length with a single model.
The other is a scheduled target noise vector that allows the system to be
robust to target distortions and to sample different transitions given fixed
keyframes. To qualitatively evaluate our method, we present a custom
MotionBuilder plugin that uses our trained model to perform in-betweening in
production scenarios. To quantitatively evaluate performance on transitions and
generalizations to longer time horizons, we present well-defined in-betweening
benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a
novel high quality motion capture dataset that is more appropriate for
transition generation. We are releasing this new dataset along with this work,
with accompanying code for reproducing our baseline results.Comment: Published at SIGGRAPH 202
A Framework for Computational Design and Adaptation of Extended Reality User Interfaces
To facilitate high quality interaction during the regular use of computing
systems, it is essential that the user interface (UI) deliver content and
components in an appropriate manner. Although extended reality (XR) is emerging
as a new computing platform, we still have a limited understanding of how best
to design and present interactive content to users in such immersive
environments. Adaptive UIs offer a promising approach for optimal presentation
in XR as the user's environment, tasks, capabilities, and preferences vary
under changing context. In this position paper, we present a design framework
for adapting various characteristics of content presented in XR. We frame these
as five considerations that need to be taken into account for adaptive XR UIs:
What?, How Much?, Where?, How?, and When?. With this framework, we review
literature on UI design and adaptation to reflect on approaches that have been
adopted or developed in the past towards identifying current gaps and
challenges, and opportunities for applying such approaches in XR. Using our
framework, future work could identify and develop novel computational
approaches for achieving successful adaptive user interfaces in such immersive
environments.Comment: 5 pages, CHI 2023 Workshop on The Future of Computational Approaches
for Understanding and Adapting User Interface
CoVR: A Large-Scale Force-Feedback Robotic Interface for Non-Deterministic Scenarios in VR
We present CoVR, a novel robotic interface providing strong kinesthetic
feedback (100 N) in a room-scale VR arena. It consists of a physical column
mounted on a 2D Cartesian ceiling robot (XY displacements) with the capacity of
(1) resisting to body-scaled users' actions such as pushing or leaning; (2)
acting on the users by pulling or transporting them as well as (3) carrying
multiple potentially heavy objects (up to 80kg) that users can freely
manipulate or make interact with each other. We describe its implementation and
define a trajectory generation algorithm based on a novel user intention model
to support non-deterministic scenarios, where the users are free to interact
with any virtual object of interest with no regards to the scenarios' progress.
A technical evaluation and a user study demonstrate the feasibility and
usability of CoVR, as well as the relevance of whole-body interactions
involving strong forces, such as being pulled through or transported.Comment: 10 pages (without references), 14 pages tota
Exploring Participatory Design Methods to Engage with Arab Communities
ArabHCI is an initiative inaugurated in CHI17 SIG Meeting that brought together 45+ HCI Arab and non-Arab researchers/practitioners who are conducting/interested in HCI within Arab communities. The goal of this workshop is to start dialogs that leverage our "insider" understanding of HCI research in the Arab context and assert our culture identity in design in order to explore challenges and opportunities for future research. In this workshop, we focus on one of the themes that derived our community discussions in most of the held events. We explore the extent to which participatory approaches in the Arab context are culturally and methodologically challenged. Our goal is to bring researchers/practitioners with success and failure stories while designing with Arab communities to discuss methods, share experiences and learned lessons. We plan to share the results of our discussions and research agenda with the wider CHI community through different social and scholarly channels
DataGarden: Exploring our Community in a VR Data Visualization
As our society is becoming increasingly data-dependent, more and more people
rely on charts and graphs to understand and communicate complex data. While
such visualizations effectively reveal meaningful trends, they unavoidably
aggregate data into points and bars that are overly simplified depictions of
ourselves and our communities. We present DataGarden, a system that supports
embodied interactions with humane data representations in an immersive VR
environment. Through the system, we explore ways to rethink the traditional
visualization approach and allow people to empathize more deeply with the
people behind the data.Comment: 10 pages, 2 figure
Machine Learning practices and infrastructures
Machine Learning (ML) systems, particularly when deployed in high-stakes
domains, are deeply consequential. They can exacerbate existing inequities,
create new modes of discrimination, and reify outdated social constructs.
Accordingly, the social context (i.e. organisations, teams, cultures) in which
ML systems are developed is a site of active research for the field of AI
ethics, and intervention for policymakers. This paper focuses on one aspect of
social context that is often overlooked: interactions between practitioners and
the tools they rely on, and the role these interactions play in shaping ML
practices and the development of ML systems. In particular, through an
empirical study of questions asked on the Stack Exchange forums, the use of
interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML
practices is explored. I find that interactive computing platforms are used in
a host of learning and coordination practices, which constitutes an
infrastructural relationship between interactive computing platforms and ML
practitioners. I describe how ML practices are co-evolving alongside the
development of interactive computing platforms, and highlight how this risks
making invisible aspects of the ML life cycle that AI ethics researchers' have
demonstrated to be particularly salient for the societal impact of deployed ML
systems
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.
Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca
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