4,216 research outputs found
Models of everywhere revisited: a technological perspective
The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
We introduce MVControl, a novel neural network architecture that enhances
existing pre-trained multi-view 2D diffusion models by incorporating additional
input conditions, e.g. edge maps. Our approach enables the generation of
controllable multi-view images and view-consistent 3D content. To achieve
controllable multi-view image generation, we leverage MVDream as our base
model, and train a new neural network module as additional plugin for
end-to-end task-specific condition learning. To precisely control the shapes
and views of generated images, we innovatively propose a new conditioning
mechanism that predicts an embedding encapsulating the input spatial and view
conditions, which is then injected to the network globally. Once MVControl is
trained, score-distillation (SDS) loss based optimization can be performed to
generate 3D content, in which process we propose to use a hybrid diffusion
prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and
our trained MVControl for additional guidance. Extensive experiments
demonstrate that our method achieves robust generalization and enables the
controllable generation of high-quality 3D content. Code available at
https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl
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