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

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

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

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