1,050 research outputs found
MFC: An open-source high-order multi-component, multi-phase, and multi-scale compressible flow solver
MFC is an open-source tool for solving multi-component, multi-phase, and bubbly compressible flows. It is capable of efficiently solving a wide range of flows, including droplet atomization, shock–bubble interaction, and bubble dynamics. We present the 5- and 6-equation thermodynamically-consistent diffuse-interface models we use to handle such flows, which are coupled to high-order interface-capturing methods, HLL-type Riemann solvers, and TVD time-integration schemes that are capable of simulating unsteady flows with strong shocks. The numerical methods are implemented in a flexible, modular framework that is amenable to future development. The methods we employ are validated via comparisons to experimental results for shock–bubble, shock–droplet, and shock–water-cylinder interaction problems and verified to be free of spurious oscillations for material-interface advection and gas–liquid Riemann problems. For smooth solutions, such as the advection of an isentropic vortex, the methods are verified to be high-order accurate. Illustrative examples involving shock–bubble-vessel-wall and acoustic–bubble-net interactions are used to demonstrate the full capabilities of MFC
Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This paper presents ER-NeRF, a novel conditional Neural Radiance Fields
(NeRF) based architecture for talking portrait synthesis that can concurrently
achieve fast convergence, real-time rendering, and state-of-the-art performance
with small model size. Our idea is to explicitly exploit the unequal
contribution of spatial regions to guide talking portrait modeling.
Specifically, to improve the accuracy of dynamic head reconstruction, a compact
and expressive NeRF-based Tri-Plane Hash Representation is introduced by
pruning empty spatial regions with three planar hash encoders. For speech
audio, we propose a Region Attention Module to generate region-aware condition
feature via an attention mechanism. Different from existing methods that
utilize an MLP-based encoder to learn the cross-modal relation implicitly, the
attention mechanism builds an explicit connection between audio features and
spatial regions to capture the priors of local motions. Moreover, a direct and
fast Adaptive Pose Encoding is introduced to optimize the head-torso separation
problem by mapping the complex transformation of the head pose into spatial
coordinates. Extensive experiments demonstrate that our method renders better
high-fidelity and audio-lips synchronized talking portrait videos, with
realistic details and high efficiency compared to previous methods.Comment: Accepted by ICCV 202
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Surface-based cortical registration is an important topic in medical image
analysis and facilitates many downstream applications. Current approaches for
cortical registration are mainly driven by geometric features, such as sulcal
depth and curvature, and often assume that registration of folding patterns
leads to alignment of brain function. However, functional variability of
anatomically corresponding areas across subjects has been widely reported,
particularly in higher-order cognitive areas. In this work, we present JOSA, a
novel cortical registration framework that jointly models the mismatch between
geometry and function while simultaneously learning an unbiased
population-specific atlas. Using a semi-supervised training strategy, JOSA
achieves superior registration performance in both geometry and function to the
state-of-the-art methods but without requiring functional data at inference.
This learning framework can be extended to any auxiliary data to guide
spherical registration that is available during training but is difficult or
impossible to obtain during inference, such as parcellations, architectonic
identity, transcriptomic information, and molecular profiles. By recognizing
the mismatch between geometry and function, JOSA provides new insights into the
future development of registration methods using joint analysis of the brain
structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal
contribution. arXiv admin note: text overlap with arXiv:2303.0159
MFC: An open-source high-order multi-component, multi-phase, and multi-scale compressible flow solver
MFC is an open-source tool for solving multi-component, multi-phase, and bubbly compressible flows. It is capable of efficiently solving a wide range of flows, including droplet atomization, shock–bubble interaction, and bubble dynamics. We present the 5- and 6-equation thermodynamically-consistent diffuse-interface models we use to handle such flows, which are coupled to high-order interface-capturing methods, HLL-type Riemann solvers, and TVD time-integration schemes that are capable of simulating unsteady flows with strong shocks. The numerical methods are implemented in a flexible, modular framework that is amenable to future development. The methods we employ are validated via comparisons to experimental results for shock–bubble, shock–droplet, and shock–water-cylinder interaction problems and verified to be free of spurious oscillations for material-interface advection and gas–liquid Riemann problems. For smooth solutions, such as the advection of an isentropic vortex, the methods are verified to be high-order accurate. Illustrative examples involving shock–bubble-vessel-wall and acoustic–bubble-net interactions are used to demonstrate the full capabilities of MFC
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