bluuebunny/crossref_metadata_2025 huggingface dataset:
The effectiveness of aerobic exercise on pain and disability in individuals with neck pain: A systematic review and meta‐analysis
Authors: de Oliveira‐Souza Ana lzabela Sobral , Kempe Marie , Grimmelsmann Sofia , Tavares Luiz Felipe , De Castro‐Carletti Ester Moreira , Andrade Angela Viegas , Dennett Liz , Von Piekartz Harry , Fuentes Contreras Jorge , Armijo‐Olivo Susan
Published: November 2024

The present review aimed to investigate the effectiveness of aerobic exercise (AE) compared to other interventions in decreasing pain intensity and reducing disability in individuals with neck pain. A systematic review (SR) of randomized controlled trials was conducted. This SR was registered in PROSPERO (CRD42021231231). Searches were conducted in five electronic databases (MEDLINE, Embase, CINAHL, Cochrane and SCOPUS). Studies were selected if they included adults over 18 years old with neck pain. The primary outcomes were pain intensity and physical function. A meta‐analysis was conducted when applicable. Cochrane RoB Tool‐2 was used to determine the risk of bias of included studies, and the certainty of the evidence was determined using the GRADE approach. Out of 4669 initial records screened, six studies published in 12 articles were included. AE was not statistically different compared to no‐treatment or other interventions (e.g., localized exercise or acupuncture) on pain intensity measured with a visual analogue scale (VAS) (mean difference (MD) [95%CI]: 5.16 mm [−6.38, 16.70]). Contrarily, strengthening exercise was better than AE for pain intensity (MD [95%CI]: −11.34 mm [−21.6, −1.09]) after treatment. However, when AE was combined with other therapy (strengthening exercises or acupuncture), the combined therapy was better than isolated treatments (MD [95%CI]: 7.71 mm [1.07, 14.35]). A high heterogeneity was observed between protocols, comparisons, and results (magnitudes and directions). In conclusion, AE had positive results only when combined with other therapies to reduce pain intensity and disability in patients with neck pain. However, the evidence is limited, low‐quality, and heterogeneous.
ASSESSMENT OF BIOLOGICAL RISKS IN INFECTIOUS DISEASES TO ENSURE BIOLOGICAL SAFETY
Authors: Myrzakhmetova B.Sh. , Zhapparova G.A. , Tlenchiyeva T.M. , Bissenbayeva K.B. , Tussipova A.A. , Zhugunissov K.D. , Kutumbetov L.B.
Published: November 2024

The territory of the Republic of Kazakhstan is unfavorable for a number of infectious diseases of animals and humans that pose an enzootic biological threat. Such nosological units include anthrax, blackleg, rabies, leukemia, brucellosis, tuberculosis, pasteurellosis, sheep pox, camel pox, CCHF, highly pathogenic avian influenza, foot-and-mouth disease, tick-borne encephalitis, influenza A and B, infectious hepatitis, Newcastle disease, seasonal flu, coronavirus infection COVID-19, etc., some of which affect only animals or humans, and some affect both animals and humans. While some of these diseases are natural focal and pose a constant biological threat, others appear from time to time, penetrating from neighboring countries or develop as a result of movement from the wild - reservoirs and hidden carriers of pathogens. A number of exotic diseases pose a real threat of penetration from outside the country due to interstate movements of people, animals, feed and animal products.
Quantifying the influence of source motion on the ray-based blind deconvolution algorithm
Authors: Touret Richard X. , Durofchalk Nicholas , Sabra Karim G.
Published: November 2024

This Letter investigates the influence of source motion on the performance of the ray-based blind deconvolution algorithm (RBD). RBD is used to estimate channel impulse responses and source signals from opportunistic sources such as shipping vessels but was derived under a stationary source assumption. A theoretical correction for Doppler from a simplified moving source model is used to quantify the biases in estimated arrival angles and travel times from RBD. This correction is numerically validated using environmental data from the SBCeX16 experiment in the Santa Barbara Channel. Implications for source localization and potential passive acoustic tomography using RBD are discussed.
Repurposing colforsin daropate to treat MYC-driven high-grade serous ovarian carcinomas
Authors: Knarr Matthew J. , Moon Jamie , Rawat Priyanka , DiFeo Analisa , Hoon David S. B. , Drapkin Ronny
Published: November 2024

High-grade serous ovarian cancer (HGSOC) is one of the deadliest cancers for women, with a low survival rate, no early detection biomarkers, a high rate of recurrence, and few therapeutic options. Forskolin, an activator of cyclic AMP signaling, has several anticancer activities, including against HGSOC, but has limited use in vivo. Its water-soluble derivative, colforsin daropate, has the same mechanism of action as forskolin and is used to treat acute heart failure. Here, we investigated the potential of colforsin daropate as a treatment for HGSOC. We found that colforsin daropate induced cell cycle arrest and apoptosis in cultured HGSOC cells and spheroids but had negligible cytotoxicity in immortalized, nontumorigenic fallopian tube secretory cells and ovarian surface epithelial cells. Colforsin daropate also prevented HGSOC cells from invading ovarian surface epithelial cell layers in culture. In vivo, colforsin daropate reduced tumor growth, synergized with cisplatin (a standard chemotherapy in ovarian cancer care), and improved host survival in subcutaneous and intraperitoneal xenograft models. These antitumor effects of colforsin daropate were mediated in part by its reduction in the abundance and transcriptional activity of the oncoprotein c-MYC, which is often increased in HGSOC. Our findings demonstrate that colforsin daropate may be a promising therapeutic that could be combined with conventional therapies to treat HGSOC.
C-di-AMP accumulation disrupts glutathione metabolism in
Listeria monocytogenes
Authors: Siletti Cheta , Freeman Matthew , Dang Hung H. , Tu Zepeng , Stevenson David M. , Amador-Noguez Daniel , Sauer John-Demian , Huynh TuAnh N.
Published: November 2024

C-di-AMP homeostasis is critical for bacterial stress response, cell wall integrity, and virulence. Except for osmotic stress response, the molecular mechanisms underlying other processes are not well defined. A
Listeria monocytogenes
mutant lacking both c-di-AMP phosphodiesterases, denoted as the ΔPDE mutant, is significantly attenuated in the mouse model of systemic infection. We utilized the ΔPDE mutant to define the molecular functions of c-di-AMP. RNAseq revealed that the ΔPDE mutant is significantly impaired for the expression of virulence genes regulated by the master transcription factor PrfA, which is activated by reduced glutathione (GSH) during infection. Subsequent quantitative gene expression analyses revealed that the ΔPDE strain is defective for PrfA-regulated gene expression both at the basal level and upon activation by GSH. We further found the ΔPDE strain to be significantly depleted for cytoplasmic GSH and impaired for GSH uptake. The ΔPDE strain was also deficient in GSH under conditions that activate GSH synthesis by the synthase GshF and upon constitutive expression of
gshF
, suggesting that c-di-AMP accumulation inhibits GSH synthesis activity or promotes GSH catabolism. A constitutively active PrfA* variant restored virulence gene expression in ΔPDE in broth cultures supplemented with GSH but did not rescue virulence defect
in vivo
. Therefore, virulence attenuation at high c-di-AMP is likely associated with defects outside of the PrfA regulon. For instance, the ΔPDE strain was sensitive to oxidative stress, a phenotype exacerbated in the absence of GshF. Our data reveal GSH metabolism as another pathway that is regulated by c-di-AMP.

IMPORTANCE

C-di-AMP regulates both bacterial pathogenesis and interactions with the host. Although c-di-AMP is essential in many bacteria, its accumulation also attenuates the virulence of many bacterial pathogens. Therefore, disrupting c-di-AMP homeostasis is a promising antibacterial treatment strategy and has inspired several studies that screened for chemical inhibitors of c-di-AMP phosphodiesterases. However, the molecular functions of c-di-AMP are still not fully defined, and the underlying mechanisms for attenuated virulence at high c-di-AMP levels are unclear. Our analyses in
Listeria monocytogenes
indicate that virulence-related defects are likely outside of the virulence gene regulon. We found c-di-AMP accumulation to impair
L. monocytogenes
virulence gene expression and disrupt GSH metabolism. Further studies are necessary to establish the relative contributions of these regulations to virulence and host adaptation.
CRISPR-Cas9-based approaches for genetic analysis and epistatic interaction studies in
Coxiella burnetii
Authors: Steiner Samuel , Roy Craig R.
Published: November 2024

Coxiella burnetii
is an obligate intracellular bacterial pathogen that replicates to high numbers in an acidified lysosome-derived vacuole. Intracellular replication requires the Dot/Icm type IVB secretion system, which translocates over 100 different effector proteins into the host cell. Screens employing random transposon mutagenesis have identified several
C. burnetii
effectors that play an important role in intracellular replication; however, the difficulty in conducting directed mutagenesis has been a barrier to the systematic analysis of effector mutants and to the construction of double mutants to assess epistatic interactions between effectors. Here, two CRISPR-Cas9 technology-based approaches were developed to study
C. burnetii
phenotypes resulting from targeted gene disruptions. CRISPRi was used to silence gene expression and demonstrated that silencing of effectors or Dot/Icm system components resulted in phenotypes similar to those of transposon insertion mutants. A CRISPR-Cas9-mediated cytosine base editing protocol was developed to generate targeted loss-of-function mutants through the introduction of premature stop codons into
C. burnetii
genes. Cytosine base editing successfully generated double mutants in a single step. A double mutant deficient in both
cig57
and
cig2
had a robust and additive intracellular replication defect when compared to either single mutant, which is consistent with Cig57 and Cig2 functioning in independent pathways that both contribute to a vacuole that supports
C. burnetii
replication. Thus, CRISPR-Cas9-based technologies expand the genetic toolbox for
C. burnetii
and will facilitate genetic studies aimed at investigating the mechanisms this pathogen uses to replicate inside host cells.

IMPORTANCE

Understanding the genetic mechanisms that enable
C. burnetii
to replicate in mammalian host cells has been hampered by the difficulty in making directed mutations. Here, a reliable and efficient system for generating targeted loss-of-function mutations in
C. burnetii
using a CRISPR-Cas9-assisted base editing approach is described. This technology was applied to make double mutants in
C. burnetii
that enabled the genetic analysis of two genes that play independent roles in promoting the formation of vacuoles that support intracellular replication. This advance will accelerate the discovery of mechanisms important for
C. burnetii
host infection and disease.
Description of the first marine-isolated member of the under-represented phylum
Gemmatimonadota
, and the environmental distribution and ecogenomics of
Gaopeijiales
ord. nov.
Authors: Ye Yu-Qi , Ye Meng-Qi , Zhang Xin-Yue , Huang You-Zhi , Zhou Zi-Yang , Feng Yan-Jun , Du Zong-Jun
Published: November 2024

The phylum
Gemmatimonadota
is widespread but rarely cultured and, in fact, there are only six described species isolated from soil, freshwater, and wastewater treatment. However, no isolates of
Gemmatimonadota
from marine environment have been described; thus, little is known about the physiology and metabolism of members of the marine lineages. In this study, four novel facultatively anaerobic bacterial strains belonging to
Gemmatimonadota
were isolated from marine sediments collected from Xiaoshi Island in Weihai, China, using an aerobic enrichment method. The integrated results of phylogenetic and phenotypic characteristics supported that these four strains represent one novel species in a novel genus, for which the name
Gaopeijia maritima
gen. nov., sp. nov. is proposed, as the first representative of novel taxa,
Gaopeijiales
ord. nov.,
Gaopeijiaceae
fam. nov. in the class
Longimicrobiia. Gaopeijiales
was detected in 22,884 out of 95,549 amplicon data sets, mainly from soil. However, the highest mean relative abundances were in sponge (0.7%) and marine sediment (0.35%), showing salt-related character. Most of the
Gaopeijiales
subgroups potentially belong to the rare bacterial biosphere. The aerobic enrichment in this study could significantly increase the relative abundance of
Gaopeijiales
(from 0.37% to 2.6%). Furthermore, the metabolic capabilities inferred from high-quality representative
Gaopeijiales
genomes/MAGs suggest that this group primarily performs chemoorganoheterotrophic metabolism with facultatively anaerobic characteristics and possesses various secondary metabolite biosynthesis gene clusters (BGCs), mirroring those observed in the four novel strains.

IMPORTANCE

Despite rapid advances in molecular and sequencing technologies, obtaining pure cultures remains a crucial research goal in microbiology, as it is essential for a deeper understanding of microbial metabolism.
Gemmatimonadota
is a widespread but rarely cultured bacterial phylum. Currently, there are only six cultured strains of this interesting group, all isolated from non-marine environments. Little is known about the physiology and metabolism of members of the marine lineages. Here we isolated and characterized four novel marine strains, and proposed a new order
Gaopeijiales
within
Gemmatimonadota
. Furthermore, the global distribution, environmental preference, and metabolic potential of
Gaopeijiales
are analyzed using public data. Our work enriches the resources available for the under-represented phylum
Gemmatimonadota
and provides insights into the physiological and metabolic characteristics of the marine lineage (
Gaopeijiales
) through culturology and omics.
Is it time to reframe resuscitation in trauma?
Authors: Carden Rich , Horner Daniel
Published: November 2024

Trauma remains a significant cause of mortality and morbidity. Non-compressible torso haemorrhage is one of the key drives of these mortality data. Our contemporary management has focused on damage control resuscitation, with a focus on haemorrhage control, haemostatic resuscitation and permissive hypotension. The evidence for permissive hypotension lacks the robustness as other treatments, such as tranexamic acid. Despite this clinicians still target arbitrary systolic blood pressure cutoffs as both goals and ceilings of therapy. In this paper, we suggest that perhaps more consideration should be given to the diastolic blood pressure in bleeding trauma patients. The diastolic blood pressure is critical for coronary perfusion, and in turn the cardiac output responsible for cerebral blood flow. We suggest that a move to reframing resuscitation in terms of physiology may change the way that we resuscitate these patients and allow for more nuanced treatment strategies.
Management of alcohol withdrawal syndrome in patients with coexistent alcohol-related liver disease
Authors: Ibrahim Lujine , Harcombe Stroma , Smith Georgia , Doyle Alexander , Forrest Ewan H
Published: November 2024

Background and aims

Treatment of alcohol withdrawal syndrome (AWS) is challenging, especially in those with alcohol-related liver disease (ArLD). Guidelines suggest using shorter-acting benzodiazepines (BZDs) but clinical evidence is sparse. We assessed the management of AWS using the Glasgow Modified Alcohol Withdrawal Scale (GMAWS), with a symptom-triggered treatment (STT) approach using lorazepam in ArLD patients.

Method

Three groups of patients were identified: group 1: patients without ArLD with AWS; group 2: ArLD patients with AWS and group 3: ArLD patients without AWS. BZD use in the first 48 hours of admission was calculated, and alcohol use disorder was assessed with the Fast Alcohol Screening Tool (FAST).

Results

Of 613 hospital episodes, 232 were in group 1, 174 in group 2 and 207 in group 3. Compared with group 1, group 2 patients had lower FAST scores (14 (13, 15)cf15 (14, 16); p=0.036), initial GMAWS (0 (0, 0)cf2 (1, 2); p<0.0001) and less BZD prescribed (30 (20, 42)cf30 (20, 42) mg; p<0.0001). STT (83%cf48%: p<0.0001) and lorazepam (79%cf22%: p<0.0001) were more common in group 2 patients. AWS treatment was not associated with a worse 180-day survival in ArLD patients (91.3% in group 2 and 80.2% in group 3).

Conclusion

Patients managed for AWS without ArLD received greater amounts of BZDs compared with those with both AWS and ArLD. There was no evidence of a survival disadvantage for those with ArLD managed for AWS. An STT approach favouring lorazepam for the management of AWS in ArLD was effective using the GMAWS guidance.
Natural Bioactive Compounds: Emerging Therapies for Hyperuricemia
Authors: Liu Yafei , Zheng Kaifeng , Wang Huanhuan , Liu Hong , Zheng Kunyang , Zhang Junjun , Han Liang , Tu Shenghao , Wang Yaoxian
Published: November 2024

Hyperuricemia is a crucial feature of metabolic syndrome, characterized by elevated uric acid that causes urate crystal deposits in joints, kidneys, and subcutaneous tissues, resulting in gout and hyperuricemic nephropathy. The primary causes of uric acid metabolism disorder include overproduction and reduced excretion. The majority of uric acid in human body is derived from the breakdown of purine nucleotides. Overproduction of uric acid can result from increased concentration or activity of xanthine oxidase, the key enzyme responsible for uric acid synthesis. Alterations in the activity of proteins responsible for uric acid reabsorption and excretion can also affect serum uric acid. Many bioactive compounds derived from natural plants have been shown to inhibit xanthine oxidase activity to reduce uric acid production, modulate the activity of transport proteins to promote uric acid excretion, or alleviate oxidative stress and inflammation through various signaling pathways. These properties have garnered significant attention from researchers. In this paper, we first introduce the pathophysiological mechanisms of hyperuricemia, then summarize bioactive compounds with urate-lowering effects, and discuss their potential applications in treating hyperuricemia and its complications.
Botany, Traditional Use, Phytochemistry, Pharmacology and Clinical Applications of Rhubarb (Rhei Radix et Rhizome): A Systematic Review
Authors: Xu Haonan , Wang Wei , Li Xinzhuo , Li Yuze , Jiang Yi , Deng Chong , Song Xiaomei , Zhang Dongdong
Published: November 2024

Rhubarb, the Rhei radix et rhizoma (Da huang) is a member of the Polygonaceae family, included in the 2020 edition of the Chinese Pharmacopoeia, and is mainly distributed in Gansu, Sichuan, Qinghai, northwestern Yunnan, and eastern Tibet. Rhei radix et rhizoma is one of the most commonly used traditional Chinese medicines, processed into drinkable tablets in different concoctions for clinical use. Phytochemical studies showed that more than 170 compounds were isolated and identified from Rhei radix et rhizoma, including anthraquinones/anthrones (major constituents), stilbenes, chromones, flavonoids, tannins, and other compounds, etc. Rhei radix et rhizoma is an herb with a long history of traditional use and many potential therapeutic benefits. It can play the role of diarrhea, clearing heat in response to different combinations and concoctions. It is mainly used for the treatment of constipation, gastrointestinal function impairment, and other diseases. In addition, Rhei radix et rhizoma has significant antitumor, anti-inflammatory, antibacterial, hepato-renal protective, neuroprotective, hypolipidemic, and immunomodulatory activities. Its active constituents have anti-tumor, anti-inflammatory, anti-inflammatory and hepato-kidney protective properties. Although it is primarily used for gastrointestinal disorders, it may also have wider applications in various inflammatory and liver disorders. Therefore, further studies are needed to explore its full potential and mechanism of action. This paper reviews the research progress of Rhei radix et rhizoma in terms of botany, traditional use, chemistry, pharmacology, and clinical studies. It aims to provide a scientific basis for in-depth research and development of Rhei radix et rhizome resources.
Real-time Large-scale Deformation of Gaussian Splatting
Authors: Gao Lin , Yang Jie , Zhang Bo-Tao , Sun Jia-Mu , Yuan Yu-Jie , Fu Hongbo , Lai Yu-Kun
Published: November 2024

Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in a real-time fashion. Gaussian Splatting (GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However, it cannot be easily deformed due to the use of discrete Gaussians and the lack of explicit topology. To address this, we develop a novel GS-based method (GaussianMesh) that enables interactive deformation. Our key idea is to design an innovative mesh-based GS representation, which is integrated into Gaussian learning and manipulation. 3D Gaussians are defined over an explicit mesh, and they are bound with each other: the rendering of 3D Gaussians guides the mesh face split for adaptive refinement, and the mesh face split directs the splitting of 3D Gaussians. Moreover, the explicit mesh constraints help regularize the Gaussian distribution, suppressing poor-quality Gaussians (
e.g.
, misaligned Gaussians, long-narrow shaped Gaussians), thus enhancing visual quality and reducing artifacts during deformation. Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh. Our method benefits from existing mesh deformation datasets for more realistic data-driven Gaussian deformation. Extensive experiments show that our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate (65 FPS on average on a single commodity GPU).
Efficient GPU Cloth Simulation with Non-distance Barriers and Subspace Reuse
Authors: Lan Lei , Lu Zixuan , Long Jingyi , Yuan Chun , Li Xuan , He Xiaowei , Wang Huamin , Jiang Chenfanfu , Yang Yin
Published: November 2024

This paper pushes the performance of cloth simulation, making the simulation interactive even for high-resolution garment models while keeping every triangle untangled. The penetration-free guarantee is inspired by the interior point method, which converts the inequality constraints to barrier potentials. We propose a major overhaul of this modality within the projective dynamics framework by leveraging an adaptive weighting mechanism inspired by barrier formulation. This approach does not depend on the distance between mesh primitives, but on the virtual life span of a collision event and thus keeps all the vertices within feasible region. Such a non-distance barrier model allows a new way to integrate collision resolution into the simulation pipeline. Another contributor to the performance boost comes from the subspace reuse strategy. This is based on the observation that low-frequency strain propagation is near orthogonal to the deformation induced by collisions or self-collisions, often of high frequency. Subspace reuse then takes care of low-frequency residuals, while high-frequency residuals can also be effectively smoothed by GPU-based iterative solvers. We show that our method outperforms existing fast cloth simulators by at least one order while producing high-quality animations of high-resolution models.
GroomCap: High-Fidelity Prior-Free Hair Capture
Authors: Zhou Yuxiao , Chai Menglei , Wang Daoye , Winberg Sebastian , Wood Erroll , Sarkar Kripasindhu , Gross Markus , Beeler Thabo
Published: November 2024

Despite recent advances in multi-view hair reconstruction, achieving strand-level precision remains a significant challenge due to inherent limitations in existing capture pipelines. We introduce
GroomCap
, a novel multi-view hair capture method that reconstructs faithful and high-fidelity hair geometry without relying on external data priors. To address the limitations of conventional reconstruction algorithms, we propose a neural implicit representation for hair volume that encodes high-resolution 3D orientation and occupancy from input views. This implicit hair volume is trained with a new volumetric 3D orientation rendering algorithm, coupled with 2D orientation distribution supervision, to effectively prevent the loss of structural information caused by undesired orientation blending. We further propose a Gaussian-based hair optimization strategy to refine the traced hair strands with a novel chained Gaussian representation, utilizing direct photometric supervision from images. Our results demonstrate that
GroomCap
is able to capture high-quality hair geometries that are not only more precise and detailed than existing methods but also versatile enough for a range of applications.
mpcMech: Multi-Point Conjugation Mechanisms
Authors: Chen Ke , Li Siqi , Song Peng , Zheng Jianmin , Liu Ligang
Published: November 2024

A mechanism is an assembly of moving parts interconnected by joints to transfer an input motion to a desired output motion. Traditionally, to generate a complex motion, mechanisms are modeled by selecting and combining a number of mechanical parts with simple shapes such as links, gears, and cams. Combining multiple mechanical parts results in a mechanism with an intricate topology, which not only complicates assembly and maintenance but also deteriorates the functionality of generating motions due to accumulation of manufacturing imprecisions. To get rid of these limitations, we study mechanisms with
a single pair
of moving parts for generating complex motions. We model the pair of moving parts as a pair of conjugate surfaces with multiple conjugation points, forming a
multi-point conjugation mechanism.

To study this new mechanism, we establish a connection between conjugate surface pairs and form-closure grasps to formulate a
dynamic form closure
condition under which one conjugate surface is able to continuously transfer the motion to the other conjugate surface by utilizing multiple conjugation points. Guided by the condition, we propose an optimization-based approach to model the geometry of a multi-point conjugation mechanism for exactly generating a user-specified motion, in 1-, 2-, or 3-DOF motion space. The core of our approach is to model multiple conjugate curve pairs that satisfy various requirements in multi-point conjugation, dynamic form closure, and surface fabricability. We demonstrate the effectiveness of our approach by modeling different classes of multi-point conjugation mechanisms to generate various motions, evaluating the mechanisms' kinematic performance with 3D printed prototypes, and presenting three applications of these mechanisms.
PuzzleAvatar: Assembling 3D Avatars from Personal Albums
Authors: Xiu Yuliang , Ye Yufei , Liu Zhen , Tzionas Dimitris , Black Michael J.
Published: November 2024

Generating
personalized
3D avatars is crucial for AR/VR. However, recent text-to-3D methods that generate avatars for celebrities or fictional characters, struggle with everyday people. Methods for faithful reconstruction typically require full-body images in controlled settings. What if users could just upload their personal "OOTD" (Outfit Of The Day) photo collection and get a faithful avatar in return? The challenge is that such casual photo collections contain diverse poses, challenging viewpoints, cropped views, and occlusion (albeit with a consistent outfit, accessories and hairstyle). We address this novel "
Album2Human
" task by developing
PuzzleAvatar
, a novel model that generates a faithful 3D avatar (in a canonical pose) from a personal OOTD album, bypassing the challenging estimation of body and camera pose. To this end, we fine-tune a foundational vision-language model (VLM) on such photos, encoding the appearance, identity, garments, hairstyles, and accessories of a person into separate learned tokens, instilling these cues into the VLM. In effect, we exploit the learned tokens as "puzzle pieces" from which we assemble a faithful, personalized 3D avatar. Importantly, we can customize avatars by simply inter-changing tokens. As a benchmark for this new task, we create a new dataset, called
PuzzleIOI
, with 41 subjects in a total of nearly 1k OOTD configurations, in challenging partial photos with paired ground-truth 3D bodies. Evaluation shows that PuzzleAvatar not only has high reconstruction accuracy, outperforming TeCH and MVDreamBooth, but also a unique scalability to album photos, and demonstrating strong robustness. Our code and data are publicly available for research purpose at
puzzleavatar.is.tue.mpg.de
Alignable Lamella Gridshells
Authors: Pellis Davide
Published: November 2024

Alignable lamella gridshells are 3D grid structures capable of collapsing into a planar strip. This feature significantly simplifies on-site assembly and also ensures compactness for efficient transport and storage. However, designing these structures still remains a challenge. This paper tackles the inverse design problem of alignable lamella gridshells leveraging concepts from differential geometry and Cartan's theory of moving frames. The study unveils that geodesic alignable gridshells, where lamellae are disposed tangentially to the surface, are limited to forming shapes isometric to surfaces of revolution. Furthermore, it demonstrates that alignable gridshells with lamellae arranged orthogonally to a surface can be realized only on a specific class of surfaces that meet a particular curvature condition along their principal curvature lines. Finally, drawing on these theoretical findings, this work introduces novel computational tools tailored for the design of these structures.
Neural Laplacian Operator for 3D Point Clouds
Authors: Pang Bo , Zheng Zhongtian , Li Yilong , Wang Guoping , Wang Peng-Shuai
Published: November 2024

The discrete Laplacian operator holds a crucial role in 3D geometry processing, yet it is still challenging to define it on point clouds. Previous works mainly focused on constructing a local triangulation around each point to approximate the underlying manifold for defining the Laplacian operator, which may not be robust or accurate. In contrast, we simply use the
K-
nearest neighbors (KNN) graph constructed from the input point cloud and learn the Laplacian operator on the KNN graph with graph neural networks (GNNs). However, the ground-truth Laplacian operator is defined on a manifold mesh with a different connectivity from the KNN graph and thus cannot be directly used for training. To train the GNN, we propose a novel training scheme by imitating the behavior of the ground-truth Laplacian operator on a set of probe functions so that the learned Laplacian operator behaves similarly to the ground-truth Laplacian operator. We train our network on a subset of ShapeNet and evaluate it across a variety of point clouds. Compared with previous methods, our method reduces the error by
an order of magnitude
and excels in handling sparse point clouds with thin structures or sharp features. Our method also demonstrates a strong generalization ability to unseen shapes. With our learned Laplacian operator, we further apply a series of Laplacian-based geometry processing algorithms directly to point clouds and achieve accurate results, enabling many exciting possibilities for geometry processing on point clouds. The code and trained models are available at https://github.com/IntelligentGeometry/NeLo.
A Generalized Ray Formulation For Wave-Optical Light Transport
Authors: Steinberg Shlomi , Ramamoorthi Ravi , Bitterli Benedikt , d'Eon Eugene , Yan Ling-Qi , Pharr Matt
Published: November 2024

Ray optics is the foundation of modern path tracing and sampling algorithms for computer graphics; crucially, it allows high-performance implementations based on ray tracing. However, many applications of interest in computer graphics and computational optics demand a more precise understanding of light: as waves. For example, accurately modelling scattering effects like diffraction or interference requires a model that provides the coherence of light waves arriving at surfaces. While recent work in Physical Light Transport [Steinberg et al. 2022; Steinberg and Yan 2021] has introduced such a model, it requires tracing light paths starting from the light sources, which is often less efficient than tracing them from the sensor, and does not allow the use of many effective importance sampling techniques.

We introduce a new model for wave optical light transport that is based on the fact that sensors aggregate the measurement of many light waves when capturing an image. This allows us to compactly represent the statistics of light waves in a
generalized ray.
Generalized rays allow sampling light paths starting from the sensor and applying sophisticated path tracing sampling techniques while still accurately modelling the wave nature of light. Our model is computationally efficient and straightforward to add to an existing path tracer; this offers the prospect of wave optics becoming the foundation of most renderers in the future. Using our model, we show that it is possible to render complex scenes under wave optics with high performance, which has not been possible with any existing method.
CBIL: Collective Behavior Imitation Learning for Fish from Real Videos
Authors: Wu Yifan , Dou Zhiyang , Ishiwaka Yuko , Ogawa Shun , Lou Yuke , Wang Wenping , Liu Lingjie , Komura Taku
Published: November 2024

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground-truth motion trajectories and struggle with authenticity, especially in high-density groups with erratic movements. In this paper, we present a scalable approach, Collective Behavior Imitation Learning (CBIL), for learning fish schooling behavior
directly from videos
, without relying on captured motion trajectories. Our method first leverages Video Representation Learning, in which a Masked Video AutoEncoder (MVAE) extracts implicit states from video inputs in a self-supervised manner. The MVAE effectively maps 2D observations to implicit states that are compact and expressive for following the imitation learning stage. Then, we propose a novel adversarial imitation learning method to effectively capture complex movements of the schools of fish, enabling efficient imitation of the distribution of motion patterns measured in the latent space. It also incorporates bio-inspired rewards alongside priors to regularize and stabilize training. Once trained, CBIL can be used for various animation tasks with the learned collective motion priors. We further show its effectiveness across different species. Finally, we demonstrate the application of our system in detecting abnormal fish behavior from in-the-wild videos.
A Cubic Barrier with Elasticity-Inclusive Dynamic Stiffness
Authors: Ando Ryoichi
Published: November 2024

This paper presents a new cubic barrier with elasticity-inclusive dynamic stiffness for penetration-free contact resolution and strain limiting. We show that our method enlarges tight strain-limiting gaps where logarithmic barriers struggle and enables highly scalable contact-rich simulation.
LVCD: Reference-based Lineart Video Colorization with Diffusion Models
Authors: Huang Zhitong , Zhang Mohan , Liao Jing
Published: November 2024

We propose the first video diffusion framework for reference-based lineart video colorization. Unlike previous works that rely solely on image generative models to colorize lineart frame by frame, our approach leverages a large-scale pretrained video diffusion model to generate colorized animation videos. This approach leads to more temporally consistent results and is better equipped to handle large motions. Firstly, we introduce
Sketch-guided ControlNet
which provides additional control to finetune an image-to-video diffusion model for controllable video synthesis, enabling the generation of animation videos conditioned on lineart. We then propose
Reference Attention
to facilitate the transfer of colors from the reference frame to other frames containing fast and expansive motions. Finally, we present a novel scheme for sequential sampling, incorporating the
Overlapped Blending Module
and
Prev-Reference Attention
, to extend the video diffusion model beyond its original fixed-length limitation for long video colorization. Both qualitative and quantitative results demonstrate that our method significantly outperforms state-of-the-art techniques in terms of frame and video quality, as well as temporal consistency. Moreover, our method is capable of generating high-quality, long temporal-consistent animation videos with large motions, which is not achievable in previous works. Our code and model are available at
https://luckyhzt.github.io/lvcd.
Inverse Rendering for Tomographic Volumetric Additive Manufacturing
Authors: Nicolet Baptiste , Wechsler Felix , Madrid-Wolff Jorge , Moser Christophe , Jakob Wenzel
Published: November 2024

Tomographic Volumetric Additive Manufacturing (TVAM) is an emerging 3D printing technology that can create complex objects in under a minute. The key idea is to project intense light patterns onto a rotating vial of photo-sensitive resin, causing polymerization where the cumulative dose of these patterns reaches the polymerization threshold. We formulate the pattern calculation as an inverse light transport problem and solve it via physically based differentiable rendering. In doing so, we address longstanding limitations of prior work by accurately modeling and correcting for scattering in composite resins, printing in non-symmetric vials, and supporting unusual printing geometries. We also introduce an improved discretization scheme that exploits the ray tracing operation to mitigate resolution-related artifacts in prints. We demonstrate the benefits of our method in real-world experiments, where our computed patterns produce prints with an improved fidelity.
Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos
Authors: Jiang Yuheng , Shen Zhehao , Hong Yu , Guo Chengcheng , Wu Yize , Zhang Yingliang , Yu Jingyi , Xu Lan
Published: November 2024

Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets in existing workflows impedes broader adoption. In this paper, we present a novel Gaussian-based approach, dubbed
DualGS
, for real-time and high-fidelity playback of complex human performance with excellent compression ratios. Our key idea in DualGS is to separately represent motion and appearance using the corresponding skin and joint Gaussians. Such an explicit disentanglement can significantly reduce motion redundancy and enhance temporal coherence. We begin by initializing the DualGS and anchoring skin Gaussians to joint Gaussians at the first frame. Subsequently, we employ a coarse-to-fine training strategy for frame-by-frame human performance modeling. It includes a coarse alignment phase for overall motion prediction as well as a fine-grained optimization for robust tracking and high-fidelity rendering. To integrate volumetric video seamlessly into VR environments, we efficiently compress motion using entropy encoding and appearance using codec compression coupled with a persistent codebook. Our approach achieves a compression ratio of up to 120 times, only requiring approximately 350KB of storage per frame. We demonstrate the efficacy of our representation through photo-realistic, free-view experiences on VR headsets, enabling users to immersively watch musicians in performance and feel the rhythm of the notes at the performers' fingertips. Project page: https://nowheretrix.github.io/DualGS/.
GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations
Authors: Teotia Kartik , Kim Hyeongwoo , Garrido Pablo , Habermann Marc , Elgharib Mohamed , Theobalt Christian
Published: November 2024

Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approaches address this challenge with computationally efficient geometry primitives in a carefully calibrated multi-view setup. Albeit producing photorealistic head renderings, they often fail to represent complex motion changes, such as the mouth interior and strongly varying head poses. We propose a new method to generate highly dynamic and deformable human head avatars from multi-view imagery in real time. At the core of our method is a hierarchical representation of head models that can capture the complex dynamics of facial expressions and head movements. First, with rich facial features extracted from raw input frames, we learn to deform the coarse facial geometry of the template mesh. We then initialize 3D Gaussians on the deformed surface and refine their positions in a fine step. We train this coarse-to-fine facial avatar model along with the head pose as learnable parameters in an end-to-end framework. This enables not only controllable facial animation via video inputs but also high-fidelity novel view synthesis of challenging facial expressions, such as tongue deformations and fine-grained teeth structure under large motion changes. Moreover, it encourages the learned head avatar to generalize towards new facial expressions and head poses at inference time. We demonstrate the performance of our method with comparisons against the related methods on different datasets, spanning challenging facial expression sequences across multiple identities. We also show the potential application of our approach by demonstrating a cross-identity facial performance transfer application. We make the code available on our
project page.
CPoser: An Optimization-after-Parsing Approach for Text-to-Pose Generation Using Large Language Models
Authors: Li Yumeng , Chen Bohong , Ren Zhong , Ding Yao-Xiang , Liu Libin , Shao Tianjia , Zhou Kun
Published: November 2024

Text-to-pose generation is challenging due to the complexity of natural language and human posture semantics. Utilizing large language models (LLMs) for text-to-pose generation is appealing due to their strong capabilities in text understanding and reasoning. However, as LLMs are designed for general-purpose language processing and not specifically trained for pose generation, it remains nontrivial to generate precise articulation targets for the full body using LLMs directly. To this end, we propose CPoser, a novel approach to harness the power of LLMs for text-to-pose generation, featuring a prompt parsing stage and a pose optimization stage. The parsing stage utilizes LLMs to turn text prompts into pose intermediate representations (Pose-IRs) through a set of predefined structured queries. These Pose-IRs explicitly describe specific pose conditions, such as squatting depth and knee bending angle, naturally forming an objective function that a target pose should satisfy. The optimization stage solves for expressive poses and hand gestures based on the Pose-IR objective function via robust optimization in a quantized pose prior space. The results are further refined to enhance naturalness and incorporate facial expressions. Experiments show that our approach effectively understands diverse text prompts for pose generation, surpassing existing text-to-pose methods.
Online Neural Denoising with Cross-Regression for Interactive Rendering
Authors: Choi Hajin , Hong Seokpyo , Ha Inwoo , Kang Nahyup , Moon Bochang
Published: November 2024

Generating a rendered image sequence through Monte Carlo ray tracing is an appealing option when one aims to accurately simulate various lighting effects. Unfortunately, interactive rendering scenarios limit the allowable sample size for such sampling-based light transport algorithms, resulting in an unbiased but noisy image sequence. Image denoising has been widely adopted as a post-sampling process to convert such noisy image sequences into biased but temporally stable ones. The state-of-the-art strategy for interactive image denoising involves devising a deep neural network and training this network via supervised learning, i.e., optimizing the network parameters using training datasets that include an extensive set of image pairs (noisy and ground truth images). This paper adopts the prevalent approach for interactive image denoising, which relies on a neural network. However, instead of supervised learning, we propose a different learning strategy that trains our network parameters on the fly, i.e., updating them online using runtime image sequences. To achieve our denoising objective with online learning, we tailor local regression to a cross-regression form that can guide robust training of our denoising neural network. We demonstrate that our denoising framework effectively reduces noise in input image sequences while robustly preserving both geometric and non-geometric edges, without requiring the manual effort involved in preparing an external dataset.
Efficient Image-Space Shape Splatting for Monte Carlo Rendering
Authors: Tong Xiaochun , Hachisuka Toshiya
Published: November 2024

A typical Monte Carlo rendering method contributes one light path only to a single pixel at a time. Reusing light paths across multiple pixels, however, can amortize the cost and improve the efficiency. The state of the art of path reuse is to employ shift mapping to reduce the cost of path reuse, while its computation cost is still proportional to the number of pixels processed in shift mapping. We propose a general framework for efficiently reusing light paths to multiple pixels arranged in arbitrary two-dimensional shapes. Our shape is defined as a set of multiple pixels, and the framework allows us to reuse light paths among pixels in a shape faster than simply evaluating all pixels via shift mapping. The key idea is to sparsely evaluate the contribution of shifted paths at random pixels within the shape and interpolate the contribution to the other pixels. We apply a debiasing estimator to ensure unbiasedness. Our method can be integrated with many existing rendering methods and brings consistent improvement over its single-pixel counterpart.
Stochastic Normal Orientation for Point Clouds
Authors: Huang Guojin , Fang Qing , Zhang Zheng , Liu Ligang , Fu Xiao-Ming
Published: November 2024

We propose a simple yet effective method to orient normals for point clouds. Central to our approach is a novel optimization objective function defined from global and local perspectives. Globally, we introduce a signed uncertainty function that distinguishes the inside and outside of the underlying surface. Moreover, benefiting from the statistics of our global term, we present a local orientation term instead of a global one. The optimization problem can be solved by the commonly used numerical optimization solver, such as L-BFGS. The capability and feasibility of our approach are demonstrated over various complex point clouds. We achieve higher practical robustness and normal quality than the state-of-the-art methods.