10 research outputs found
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
We present CASE, an efficient and effective framework that learns
conditional Adversarial Skill Embeddings for physics-based characters. Our
physically simulated character can learn a diverse repertoire of skills while
providing controllability in the form of direct manipulation of the skills to
be performed. CASE divides the heterogeneous skill motions into distinct
subsets containing homogeneous samples for training a low-level conditional
model to learn conditional behavior distribution. The skill-conditioned
imitation learning naturally offers explicit control over the character's
skills after training. The training course incorporates the focal skill
sampling, skeletal residual forces, and element-wise feature masking to balance
diverse skills of varying complexities, mitigate dynamics mismatch to master
agile motions and capture more general behavior characteristics, respectively.
Once trained, the conditional model can produce highly diverse and realistic
skills, outperforming state-of-the-art models, and can be repurposed in various
downstream tasks. In particular, the explicit skill control handle allows a
high-level policy or user to direct the character with desired skill
specifications, which we demonstrate is advantageous for interactive character
animation.Comment: SIGGRAPH Asia 202
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
In this paper, we introduce a set of effective TOken REduction (TORE)
strategies for Transformer-based Human Mesh Recovery from monocular images.
Current SOTA performance is achieved by Transformer-based structures. However,
they suffer from high model complexity and computation cost caused by redundant
tokens. We propose token reduction strategies based on two important aspects,
i.e., the 3D geometry structure and 2D image feature, where we hierarchically
recover the mesh geometry with priors from body structure and conduct token
clustering to pass fewer but more discriminative image feature tokens to the
Transformer. As a result, our method vastly reduces the number of tokens
involved in high-complexity interactions in the Transformer, achieving
competitive accuracy of shape recovery at a significantly reduced computational
cost. We conduct extensive experiments across a wide range of benchmarks to
validate the proposed method and further demonstrate the generalizability of
our method on hand mesh recovery. Our code will be publicly available once the
paper is published
Activity Recognition Based on Millimeter-Wave Radar by Fusing Point Cloud and Range–Doppler Information
Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range–Doppler information from radar echo to extract activity features. In this paper, we propose a multi-model deep learning approach to fuse the features of both point clouds and range–Doppler for classifying six activities, i.e., boxing, jumping, squatting, walking, circling, and high-knee lifting, based on a millimeter-wave radar. We adopt a CNN–LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range–Doppler. Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. The evaluation result based on the dataset shows that this method has higher accuracy than utilizing the two kinds of information separately and achieves a recognition accuracy of 97.26%, which is about 1% higher than other networks with only one kind of data as input
Activity Recognition Based on Millimeter-Wave Radar by Fusing Point Cloud and Range–Doppler Information
Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range–Doppler information from radar echo to extract activity features. In this paper, we propose a multi-model deep learning approach to fuse the features of both point clouds and range–Doppler for classifying six activities, i.e., boxing, jumping, squatting, walking, circling, and high-knee lifting, based on a millimeter-wave radar. We adopt a CNN–LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range–Doppler. Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. The evaluation result based on the dataset shows that this method has higher accuracy than utilizing the two kinds of information separately and achieves a recognition accuracy of 97.26%, which is about 1% higher than other networks with only one kind of data as input
Kaempferol-3-O-Glucuronide Ameliorates Non-Alcoholic Steatohepatitis in High-Cholesterol-Diet-Induced Larval Zebrafish and HepG2 Cell Models via Regulating Oxidation Stress
NAFLD (non-alcoholic fatty liver disease) is one of the most prominent liver diseases in the world. As a metabolic-related disease, the development of NAFLD is closely associated with various degrees of lipid accumulation, oxidation, inflammation, and fibrosis. Ilex chinensis Sims is a form of traditional Chinese medicine which is used to treat bronchitis, burns, pneumonia, ulceration, and chilblains. Kaempferol-3-O-glucuronide (K3O) is a natural chemical present in Ilex chinensis Sims. This study was designed to investigate the antioxidative, fat metabolism-regulating, and anti-inflammatory potential of K3O. A high-cholesterol diet (HCD) was used to establish steatosis in larval zebrafish, whereby 1mM free fatty acid (FFA) was used to induce lipid accumulation in HepG2 cells, while H2O2 was used to induce oxidative stress in HepG2. The results of this experiment showed that K3O reduced lipid accumulation and the level of reactive oxygen species (ROS) both in vivo (K3O, 40 μM) and in vitro (K3O, 20 μM). Additionally, K3O (40 μM) reduced neutrophil aggregation in vivo. K3O (20 μM) also decreased the level of malondialdehyde (MDA) and significantly increased the level of glutathione peroxidase (GSH-px) in both the HCD-induced larval zebrafish model and H2O2-exposed HepG2 cells. In the mechanism study, keap1, nrf2, tnf-α, and il-6 mRNA were all significantly reversed by K3O (20 μM) in zebrafish. Changes in Keap1 and Nrf2 mRNA expression were also detected in H2O2-exposed HepG2 cells after they were treated with K3O (20 μM). In conclusion, K3O exhibited a reduction in oxidative stress and lipid peroxidation, and this may be related to the Nrf2/Keap1 pathway in the NAFLD larval zebrafish model
CEPC Conceptual Design Report: Volume 2 - Physics & Detector
The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios
CEPC Conceptual Design Report: Volume 2 - Physics & Detector
The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios
CEPC Conceptual Design Report: Volume 2 - Physics & Detector
The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios