Object Motion Guided Human Motion Synthesis

Modeling human behaviors in contextual environments has a wide range of applications in character animation, embodied AI, VR/AR, and robotics. In real-world scenarios, humans frequently interact with the environment and manipulate various objects to complete daily tasks. In this work, we study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework that can generate full-body manipulation behaviors from only the object motion. Since naively applying diffusion models fails to precisely enforce contact constraints between the hands and the object, OMOMO learns two separate denoising processes to first predict hand positions from object motion and subsequently synthesize full-body poses based on the predicted hand positions. By employing the hand positions as an intermediate representation between the two denoising processes, we can explicitly enforce contact constraints, resulting in more physically plausible manipulation motions. With the learned model, we develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated. Through extensive experiments, we demonstrate the effectiveness of our proposed pipeline and its ability to generalize to unseen objects. Additionally, as high-quality human-object interaction datasets are scarce, we collect a large-scale dataset consisting of 3D object geometry, object motion, and human motion. Our dataset contains human-object interaction motion for 15 objects, with a total duration of approximately 10 hours.Comment: SIGGRAPH Asia 202

Ego-Body Pose Estimation via Ego-Head Pose Estimation

Estimating 3D human motion from an egocentric video sequence is critical to human behavior understanding and applications in VR/AR. However, naively learning a mapping between egocentric videos and human motions is challenging, because the user's body is often unobserved by the front-facing camera placed on the head of the user. In addition, collecting large-scale, high-quality datasets with paired egocentric videos and 3D human motions requires accurate motion capture devices, which often limit the variety of scenes in the videos to lab-like environments. To eliminate the need for paired egocentric video and human motions, we propose a new method, Ego-Body Pose Estimation via Ego-Head Pose Estimation (EgoEgo), that decomposes the problem into two stages, connected by the head motion as an intermediate representation. EgoEgo first integrates SLAM and a learning approach to estimate accurate head motion. Then, taking the estimated head pose as input, it leverages conditional diffusion to generate multiple plausible full-body motions. This disentanglement of head and body pose eliminates the need for training datasets with paired egocentric videos and 3D human motion, enabling us to leverage large-scale egocentric video datasets and motion capture datasets separately. Moreover, for systematic benchmarking, we develop a synthetic dataset, AMASS-Replica-Ego-Syn (ARES), with paired egocentric videos and human motion. On both ARES and real data, our EgoEgo model performs significantly better than the state-of-the-art.Comment: project website: https://lijiaman.github.io/projects/egoego

Transcriptomic profile of premature ovarian insufficiency with RNA-sequencing

IntroductionThis study aimed to explore the transcriptomic profile of premature ovarian insufficiency (POI) by investigating alterations in gene expression.MethodsA total of sixty-one women, comprising 31 individuals with POI in the POI group and 30 healthy women in the control group (HC group), aged between 24 and 40 years, were recruited for this study. The transcriptomic profiles of peripheral blood samples from all study subjects were analyzed using RNA-sequencing.ResultsThe results revealed 39 differentially expressed genes in individuals with POI compared to healthy controls, with 10 upregulated and 29 downregulated genes. Correlation analysis highlighted the relationship between the expression of SLC25A39, CNIH3, and PDZK1IP1 and hormone levels. Additionally, an effective classification model was developed using SLC25A39, CNIH3, PDZK1IP1, SHISA4, and LOC389834. Functional enrichment analysis demonstrated the involvement of these differentially expressed genes in the “haptoglobin-hemoglobin complex,” while KEGG pathway analysis indicated their participation in the “Proteoglycans in cancer” pathway.ConclusionThe identified genes could play a crucial role in characterizing the genetic foundation of POI, potentially serving as valuable biomarkers for enhancing disease classification accuracy

CIRCLE: Capture In Rich Contextual Environments

Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics-based motion capture systems are not suited for simultaneously capturing human movements and complex scenes. The lack of rich contextual 3D human motion datasets presents a roadblock to creating high-quality generative human motion models. We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world while being motion captured in the real world. Our system enables rapid collection of high-quality human motion in highly diverse scenes, without the concern of occlusion or the need for physical scene construction in the real world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information. Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes. To download the data please visit https://stanford-tml.github.io/circle_dataset/

Effect of orthostatic hypotension on long-term prognosis of elderly patients with stable coronary artery disease: a retrospective cohort study

BackgroundThe long-term prognosis of patients with stable coronary artery disease (CAD) combined with orthostatic hypotension (OH) has rarely been reported. This research was designed to examine whether OH increases the risk of all-cause mortality and cardiovascular death among patients with stable CAD.MethodsWe retrospectively analyzed retired military personnel over 65 years of age who were hospitalized at the General Hospital of Southern Theater Command of the Chinese People’s Liberation Army between March and July 2010. A total of 924 patients with stable CAD were included, among whom 263 had OH. The risk of all-cause mortality and cardiovascular death in OH and non-OH groups were analyzed with the Cox proportional hazards models, and restricted cubic spline plots were utilized for subgroup analyses. Furthermore, competing risk models were applied for sensitivity analyses.ResultsThe median age of the patients was 82.00 (80.00–85.00) years. Over 159 months of follow-up, the loss to follow-up rate was 2.27%, and all-cause mortality was observed in 574 (63.57%) patients, including 184 with OH. Moreover, cardiovascular death occurred in 127 patients (13.73%), with 58 cases associated with OH. Although the relationship between OH and all-cause mortality was non-significant [body mass index (BMI) < 25 group, adjusted hazard ratio (HR) = 1.10 with a 95% confidence interval (CI): 0.82–1.40; BMI ≥ 25 group, adjusted HR = 1.30, 95% CI: 0.98–1.70], it was independently related to a growing risk of cardiovascular death (adjusted HR = 1.80, 95% CI: 1.20–2.60). This finding was further validated by using a competing risk model (subdistribution HR = 1.74, 95% CI: 1.22–2.49). Moreover, age, low-density lipoprotein cholesterol, and frequency of hospital admissions were identified as risk factors of cardiovascular death among patients with OH (P < 0.05).ConclusionOur study, based on retired military personnel with stable CAD, found that OH led to a significantly higher risk of cardiovascular death, but it was not noticeably associated with all-cause mortality on long-term prognosis

Reliability of foot posture index (FPI-6) for evaluating foot posture in patients with knee osteoarthritis

Objective: To determine the reliability of FPI-6 in the assessment of foot posture in patients with knee osteoarthritis (KOA).Methods: Thirty volunteers with KOA (23 females, 7 males) were included in this study, assessed by two raters and at three different moments. Inter-rater and test-retest reliability were assessed with Cohen’s Weighted Kappa (Kw) and Intraclass Correlation Coefficient (ICC). Bland-Altman plots and respective 95% limits of agreement (LOA) were used to assess both inter-rater and test-retest agreement and identify systematic bias. Moreover, the internal consistency of FPI-6 was assessed by Spearman’s correlation coefficient.Results: FPI-6 total score showed a substantial inter-rater (Kw = .66) and test-retest reliability (Kw = .72). The six items of FPI-6 demonstrated inter-rater and test-retest reliability varying from fair to substantial (Kw = .33 to .76 and Kw = .40 to .78, respectively). Bland-Altman plots and respective 95% LOA indicated that there appeared no systematic bias and the acceptable agreement of FPI-6 total score for inter-rater and test-retest was excellent. There was a statistically significant positive correlation between each item and the total score of FPI-6, which indicated that FPI-6 had good internal consistency.Conclusion: In conclusion, the reliability of FPI-6 total score and the six items of FPI-6 were fair to substantial. The results can provide a reliable way for clinicians and researchers to implement the assessment of foot posture in patients with KOA

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

BackgroundStroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study designA case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone