152 research outputs found
Building Transversal Skills among Marriage Migrants in Japan: Japanese Language and Communication Skills in Local Multilingual Classrooms
The 15th Next-Generation Global Workshopテーマ/Theme: Making care visible and sustainable: imaginations for the future日程/Date: 24-25 September, 2022開催場所/Venue: The workshop will be held Online第15回次世代グローバルワークショッ
THE INFLUENCE OF GREEN BUILDING DESIGN ON CONSUMERS’ PURCHASE PSYCHOLOGY FROM THE PERSPECTIVE OF PSYCHOLOGY
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
Privacy-sensitive data is stored in autonomous vehicles, smart devices, or
sensor nodes that can move around with making opportunistic contact with each
other. Federation among such nodes was mainly discussed in the context of
federated learning with a centralized mechanism in many works. However, because
of multi-vendor issues, those nodes do not want to rely on a specific server
operated by a third party for this purpose. In this paper, we propose a
wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative
machine learning organized by the nodes physically nearby. WAFL can develop
generalized models from Non-IID datasets stored in distributed nodes locally by
exchanging and aggregating them with each other over opportunistic node-to-node
contacts. In our benchmark-based evaluation with various opportunistic
networks, WAFL has achieved higher accuracy of 94.8-96.3% than the
self-training case of 84.7%. All our evaluation results show that WAFL can
train and converge the model parameters from highly-partitioned Non-IID
datasets over opportunistic networks without any centralized mechanisms.Comment: 14 pages, 8 figures, 2 table
Learning Controllable 3D Diffusion Models from Single-view Images
Diffusion models have recently become the de-facto approach for generative
modeling in the 2D domain. However, extending diffusion models to 3D is
challenging due to the difficulties in acquiring 3D ground truth data for
training. On the other hand, 3D GANs that integrate implicit 3D representations
into GANs have shown remarkable 3D-aware generation when trained only on
single-view image datasets. However, 3D GANs do not provide straightforward
ways to precisely control image synthesis. To address these challenges, We
present Control3Diff, a 3D diffusion model that combines the strengths of
diffusion models and 3D GANs for versatile, controllable 3D-aware image
synthesis for single-view datasets. Control3Diff explicitly models the
underlying latent distribution (optionally conditioned on external inputs),
thus enabling direct control during the diffusion process. Moreover, our
approach is general and applicable to any type of controlling input, allowing
us to train it with the same diffusion objective without any auxiliary
supervision. We validate the efficacy of Control3Diff on standard image
generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various
conditioning inputs such as images, sketches, and text prompts. Please see the
project website (\url{https://jiataogu.me/control3diff}) for video comparisons.Comment: work in progres
Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors
We propose a new method for learning a generalized animatable neural human
representation from a sparse set of multi-view imagery of multiple persons. The
learned representation can be used to synthesize novel view images of an
arbitrary person from a sparse set of cameras, and further animate them with
the user's pose control. While existing methods can either generalize to new
persons or synthesize animations with user control, none of them can achieve
both at the same time. We attribute this accomplishment to the employment of a
3D proxy for a shared multi-person human model, and further the warping of the
spaces of different poses to a shared canonical pose space, in which we learn a
neural field and predict the person- and pose-dependent deformations, as well
as appearance with the features extracted from input images. To cope with the
complexity of the large variations in body shapes, poses, and clothing
deformations, we design our neural human model with disentangled geometry and
appearance. Furthermore, we utilize the image features both at the spatial
point and on the surface points of the 3D proxy for predicting person- and
pose-dependent properties. Experiments show that our method significantly
outperforms the state-of-the-arts on both tasks. The video and code are
available at https://talegqz.github.io/neural_novel_actor
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