iOrthoPredictor

Yang, Lingchen; Shi, Zefeng; Wu, Yiqian; Li, Xiang; Zhou, Kun; Fu, Hongbo; Zheng, Youyi

doi:10.1145/3414685.3417771

Cited by 11 publications

(4 citation statements)

References 73 publications

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“…The initial methods [CSW*16, LHM*18, HSW*17] for monocular hair reconstruction relied on a database retrieval. In contrast, recent methods train neural architectures to regress hair shape directly [ZHX*18, SHM*18, YSZZ19]. The approaches targeting high‐quality eye and ear reconstruction follow face 3DMM methods by building separate 3DMMs for eyes [BBGB16, WBM*16, PVO*20] and ears [ZEJ*16, DPS18, PVO*20].…”

Section: State‐of‐the‐art Methodsmentioning

confidence: 99%

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Tretschk

Navami

Mallikarjun

et al. 2023

Computer Graphics Forum

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3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D image observations is a long‐standing and actively researched area of computer vision and graphics. It is an ill‐posed inverse problem, since—without additional prior assumptions—it permits infinitely many solutions leading to accurate projection to the input 2D images. Non‐rigid reconstruction is a foundational building block for downstream applications like robotics, AR/VR, or visual content creation. The key advantage of using monocular cameras is their omnipresence and availability to the end users as well as their ease of use compared to more sophisticated camera set‐ups such as stereo or multi‐view systems. This survey focuses on state‐of‐the‐art methods for dense non‐rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views. It reviews the fundamentals of 3D reconstruction and deformation modeling from 2D image observations. We then start from general methods—that handle arbitrary scenes and make only a few prior assumptions—and proceed towards techniques making stronger assumptions about the observed objects and types of deformations (e.g. human faces, bodies, hands, and animals). A significant part of this STAR is also devoted to classification and a high‐level comparison of the methods, as well as an overview of the datasets for training and evaluation of the discussed techniques. We conclude by discussing open challenges in the field and the social aspects associated with the usage of the reviewed methods.

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Section: State‐of‐the‐art Methodsmentioning

confidence: 99%

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Tretschk

Navami

Mallikarjun

et al. 2023

Computer Graphics Forum

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“…Tooth Feature Representation. Deep learning-based dental tasks using the intraoral scan model include tooth segmentation (Cui et al 2021;Qiu et al 2022;Cui et al 2022), tooth classification (Ma et al 2020), tooth landmark/axis detection (Wei et al 2022;Yf et al 2022), tooth alignment target prediction (Wei et al 2020;Yang et al 2020;Wang et al 2022), and so on (Song et al 2021;Zhang et al 2022). Most of them first take the segmented point cloud of an intraoral scan model as input and then utilize the point cloud feature extraction network (Qi et al 2017a,b;Wu, Qi, and Fuxin 2019;Wang et al 2019) to extract tooth point cloud features for downstream works.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

Fan,

Wei,

Wang

et al. 2024

AAAI

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Tooth motion generation is an essential task in digital orthodontic treatment for precise and quick dental healthcare, which aims to generate the whole intermediate tooth motion process given the initial pathological and target ideal tooth alignments. Most prior works for multi-agent motion planning problems usually result in complex solutions. Moreover, the occlusal relationship between upper and lower teeth is often overlooked. In this paper, we propose a collaborative tooth motion diffusion model. The critical insight is to remodel the problem as a diffusion process. In this sense, we model the whole tooth motion distribution with a diffusion model and transform the planning problem into a sampling process from this distribution. We design a tooth latent representation to provide accurate conditional guides consisting of two key components: the tooth frame represents the position and posture, and the tooth latent shape code represents the geometric morphology. Subsequently, we present a collaborative diffusion model to learn the multi-tooth motion distribution based on inter-tooth and occlusal constraints, which are implemented by graph structure and new loss functions, respectively. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods.

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“…By doing so, they apply weaker normalization, based on the expected feature statistics rather than exact signal strength, and the network no longer needs to hide signal strength information ‐ which in turn makes the blob‐shaped artifacts disappear. This technique has also been shown to promote disentanglement between geometry and appearance in other scenarios [YSW*20].…”

Section: Stylegan Architecturesmentioning

confidence: 99%

State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN

Bermano

Gal

Alaluf

et al. 2022

Computer Graphics Forum

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Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.

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iOrthoPredictor

Cited by 11 publications

References 73 publications

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN

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