86 research outputs found
Motion In-Betweening with Phase Manifolds
This paper introduces a novel data-driven motion in-betweening system to
reach target poses of characters by making use of phases variables learned by a
Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network
model, in which the phases cluster movements in both space and time with
different expert weights. Each generated set of weights then produces a
sequence of poses in an autoregressive manner between the current and target
state of the character. In addition, to satisfy poses which are manually
modified by the animators or where certain end effectors serve as constraints
to be reached by the animation, a learned bi-directional control scheme is
implemented to satisfy such constraints. The results demonstrate that using
phases for motion in-betweening tasks sharpen the interpolated movements, and
furthermore stabilizes the learning process. Moreover, using phases for motion
in-betweening tasks can also synthesize more challenging movements beyond
locomotion behaviors. Additionally, style control is enabled between given
target keyframes. Our proposed framework can compete with popular
state-of-the-art methods for motion in-betweening in terms of motion quality
and generalization, especially in the existence of long transition durations.
Our framework contributes to faster prototyping workflows for creating animated
character sequences, which is of enormous interest for the game and film
industry.Comment: 17 pages, 11 figures, conferenc
Uncertainty Estimation in Instance Segmentation with Star-convex Shapes
Instance segmentation has witnessed promising advancements through deep
neural network-based algorithms. However, these models often exhibit incorrect
predictions with unwarranted confidence levels. Consequently, evaluating
prediction uncertainty becomes critical for informed decision-making. Existing
methods primarily focus on quantifying uncertainty in classification or
regression tasks, lacking emphasis on instance segmentation. Our research
addresses the challenge of estimating spatial certainty associated with the
location of instances with star-convex shapes. Two distinct clustering
approaches are evaluated which compute spatial and fractional certainty per
instance employing samples by the Monte-Carlo Dropout or Deep Ensemble
technique. Our study demonstrates that combining spatial and fractional
certainty scores yields improved calibrated estimation over individual
certainty scores. Notably, our experimental results show that the Deep Ensemble
technique alongside our novel radial clustering approach proves to be an
effective strategy. Our findings emphasize the significance of evaluating the
calibration of estimated certainties for model reliability and decision-making
Uptake and fecal excretion of Coxiella burnetii by Ixodes ricinus and Dermacentor marginatus ticks
Background:
The bacterium Coxiella burnetii is the etiological agent of Q fever and is mainly transmitted via inhalation of infectious aerosols. DNA of C. burnetii is frequently detected in ticks, but the role of ticks as vectors in the epidemiology of this agent is still controversial. In this study, Ixodes ricinus and Dermacentor marginatus adults as well as I. ricinus nymphs were fed on blood spiked with C. burnetii in order to study the fate of the bacterium within putative tick vectors.
Methods:
Blood-feeding experiments were performed in vitro in silicone-membrane based feeding units. The uptake, fecal excretion and transstadial transmission of C. burnetii was examined by quantitative real-time PCR as well as cultivation of feces and crushed tick filtrates in L-929 mouse fibroblast cells and cell-free culture medium.
Results:
Ticks successfully fed in the feeding system with engorgement rates ranging from 29% (D. marginatus) to 64% (I. ricinus adults). Coxiella burnetii DNA was detected in the feces of both tick species during and after feeding on blood containing 105 or 106 genomic equivalents per ml blood (GE/ml), but not when fed on blood containing only 104 GE/ml. Isolation and cultivation demonstrated the infectivity of C. burnetii in shed feces. In 25% of the I. ricinus nymphs feeding on inoculated blood, a transstadial transmission to the adult stage was detected. Females that molted from nymphs fed on inoculated blood excreted C. burnetii of up to 106 genomic equivalents per mg of feces.
Conclusions:
These findings show that transstadial transmission of C. burnetii occurs in I. ricinus and confirm that I. ricinus is a potential vector for Q fever. Transmission from both tick species might occur by inhalation of feces containing high amounts of viable C. burnetii rather than via tick bites
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Replicating a user's pose from only wearable sensors is important for many
AR/VR applications. Most existing methods for motion tracking avoid environment
interaction apart from foot-floor contact due to their complex dynamics and
hard constraints. However, in daily life people regularly interact with their
environment, e.g. by sitting on a couch or leaning on a desk. Using
Reinforcement Learning, we show that headset and controller pose, if combined
with physics simulation and environment observations can generate realistic
full-body poses even in highly constrained environments. The physics simulation
automatically enforces the various constraints necessary for realistic poses,
instead of manually specifying them as in many kinematic approaches. These hard
constraints allow us to achieve high-quality interaction motions without
typical artifacts such as penetration or contact sliding. We discuss three
features, the environment representation, the contact reward and scene
randomization, crucial to the performance of the method. We demonstrate the
generality of the approach through various examples, such as sitting on chairs,
a couch and boxes, stepping over boxes, rocking a chair and turning an office
chair. We believe these are some of the highest-quality results achieved for
motion tracking from sparse sensor with scene interaction
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
With the recent surge in popularity of AR/VR applications, realistic and
accurate control of 3D full-body avatars has become a highly demanded feature.
A particular challenge is that only a sparse tracking signal is available from
standalone HMDs (Head Mounted Devices), often limited to tracking the user's
head and wrists. While this signal is resourceful for reconstructing the upper
body motion, the lower body is not tracked and must be synthesized from the
limited information provided by the upper body joints. In this paper, we
present AGRoL, a novel conditional diffusion model specifically designed to
track full bodies given sparse upper-body tracking signals. Our model is based
on a simple multi-layer perceptron (MLP) architecture and a novel conditioning
scheme for motion data. It can predict accurate and smooth full-body motion,
particularly the challenging lower body movement. Unlike common diffusion
architectures, our compact architecture can run in real-time, making it
suitable for online body-tracking applications. We train and evaluate our model
on AMASS motion capture dataset, and demonstrate that our approach outperforms
state-of-the-art methods in generated motion accuracy and smoothness. We
further justify our design choices through extensive experiments and ablation
studies.Comment: CVPR 2023, project page: https://dulucas.github.io/agrol
Hierarchical Planning and Control for Box Loco-Manipulation
Humans perform everyday tasks using a combination of locomotion and
manipulation skills. Building a system that can handle both skills is essential
to creating virtual humans. We present a physically-simulated human capable of
solving box rearrangement tasks, which requires a combination of both skills.
We propose a hierarchical control architecture, where each level solves the
task at a different level of abstraction, and the result is a physics-based
simulated virtual human capable of rearranging boxes in a cluttered
environment. The control architecture integrates a planner, diffusion models,
and physics-based motion imitation of sparse motion clips using deep
reinforcement learning. Boxes can vary in size, weight, shape, and placement
height. Code and trained control policies are provided
Effects of dietary menthol-rich bioactive lipid compounds on zootechnical traits, blood variables and gastrointestinal function in growing sheep
Background
The present study aimed at investigating the influence of 90% menthol-containing plant bioactive lipid compounds (PBLC, essential oils) on growth performance, blood haematological and biochemical profile, and nutrient absorption in sheep. Twenty-four growing Suffolk sheep were allotted into three dietary treatments: Control (without PBLC), lower dose of PBLC (PBLC-L; 80 mg/d) and higher dose of PBLC (PBLC-H; 160 mg/d). Sheep in all groups were fed meadow hay ad libitum plus 600 g/d of concentrate pellets for 28 d.
Results
Average daily gain was not affected by treatment. Feeding of PBLC increased hay and total feed intake per kg body weight (P < 0.05). Counts of total leucocytes, lymphocytes and monocytes were not different among treatments. However, neutrophil count decreased (P < 0.05) in PBLC-H with a similar trend in PBLC-L (P < 0.10). Concentrations of glucose, bilirubin, triglycerides, cholesterol, urea and magnesium in serum were not different among sheep fed different doses of PBLC. However, serum calcium concentration tended to increase in PBLC-H (P < 0.10) and serum concentrations of aspartate & asparagine (P < 0.01) and glutamate & glutamine (P < 0.05) increased linearly with increasing PBLC dose. In ruminal epithelia isolated from the rumen after killing, baseline conductance (Gt; P < 0.05) and short-circuit current (Isc; P < 0.01) increased in both PBLC groups. Ruminal uptakes of glucose and methionine in the presence of Na+ were not affected by the dietary PBLC supplementation. In the absence of Na+, however, glucose and methionine uptakes increased (P < 0.05) in PBLC-H. In the jejunum, Isc tended to increase in PBLC-H (P < 0.10), but baseline Gt was not affected. Intestinal uptakes of glucose and methionine were not influenced by PBLC in the presence or absence of Na+.
Conclusion
The results suggest that menthol-rich PBLC increase feed intake, and passive ion and nutrient transport, the latter specifically in the rumen. They also increased serum concentrations of urea precursor amino acids and tended to increase serum calcium concentrations. Future studies will have to show whether some of these findings might be commonly linked to a stimulation of transient receptor potential (TRP) channels in the gastrointestinal tract
Wenn Kunden bewertet werden - Eine empirische Untersuchung der Auswirkungen von Kundenbewertungen in Plattformmärkten
Plattformbasierte Geschäftsmodelle setzen zur Verringerung von Risiken und Unsicherheiten zwischen Kunden und Dienstleistern vermehrt zweiseitige Bewertungssysteme ein. - In diesen zweiseitigen Bewertungssystemen bewerten nicht nur Kunden die Dienstleistenden, sondern auch Kunden erhalten von Dienstleistenden Kundenbewertungen zu ihrem Verhalten in einer Transaktion. - Aufgrund der hohen Relevanz von Plattformen in der digitalen Wirtschaft ist es wichtig, die Reaktionen von Kunden auf diese Kundenbewertungen zu untersuchen, und die Auswirkungen der Bewertungen auf die Beziehungen innerhalb von Plattformmärkten zu erforschen
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