Sensitivity-optimized rigging for example-based real-time clothing synthesis

Xu, Weiwei; Umentani, Nobuyuki; Chao, Qianwen; Mao, Jiandong; Jin, Xiaogang; Tong, Xin

doi:10.1145/2601097.2601136

Cited by 62 publications

(55 citation statements)

References 39 publications

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“…Multiple works, both well established [LCF00, SRIC01] and recent [BODO18, CO18], propose to model surface deformations as a function of pose. Similar to them, some existing data‐driven methods for clothing animation also use the underlying kinematic skeletal model to drive the garment deformation [KV08, WHRO10, GRH*12, XUC*14, HTC*14]. Kim and Vendrovsky [KV08] first introduced a pose‐space deformation approach that uses a skeletal pose as subspace domain.…”

Section: Related Workmentioning

confidence: 99%

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Learning‐Based Animation of Clothing for Virtual Try‐On

Santesteban

Otaduy

Casas

2019

Computer Graphics Forum

162

225

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This paper presents a learning‐based clothing animation method for highly efficient virtual try‐on simulation. Given a garment, we preprocess a rich database of physically‐based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning‐based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try‐on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.

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Section: Related Workmentioning

confidence: 99%

“…Xu et al . [XUC*14] used a precomputed dataset to mix and match parts of different samples to synthesize a garment mesh that matches the current pose.…”

Section: Related Workmentioning

confidence: 99%

Learning‐Based Animation of Clothing for Virtual Try‐On

Santesteban

Otaduy

Casas

2019

Computer Graphics Forum

162

225

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“…Simulation of clothing has been investigated for more than 30 years. Some works focus on super realistic cloth simulation results using millions of triangles [60,54,27], while the others concentrate on improving the fidelity for real-time simulation [51,41,40,19,18,29,31,62,14,20,65,54]. Physics-based mass-spring models [51] or position based dynamics [41,40] are commonly used for simulation of cloth.…”

Section: Related Workmentioning

confidence: 99%

SimulCap : Single-View Human Performance Capture With Cloth Simulation

Zheng

Zhong

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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This paper proposes a new method for live freeviewpoint human performance capture with dynamic details (e.g., cloth wrinkles) using a single RGBD camera. Our main contributions are: (i) a multi-layer representation of garments and body, and (ii) a physics-based performance capture procedure. We first digitize the performer using multi-layer surface representation, which includes the undressed body surface and separate clothing meshes. For performance capture, we perform skeleton tracking, cloth simulation, and iterative depth fitting sequentially for the incoming frame. By incorporating cloth simulation into the performance capture pipeline, we can simulate plausible cloth dynamics and cloth-body interactions even in the occluded regions, which was not possible in previous capture methods. Moreover, by formulating depth fitting as a physical process, our system produces cloth tracking results consistent with the depth observation while still maintaining physical constraints. Results and evaluations show the effectiveness of our method. Our method also enables new types of applications such as cloth retargeting, freeviewpoint video rendering and animations.

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“…Recent dressing simulations [6]- [9] can reproduce detailed drapes or folds of garments fitted on various different body shapes. Whereas these simulations often use a 3D avatar as a 3D human model, customer's own 3D model can be generated using depth camera, e.g., Microsoft Kinect, for whole-body scan [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Image-Based Virtual Fitting System with Garment Image Reshaping

Yamada

Hirose

Kanamori

et al. 2014

2014 International Conference on Cyberworlds

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Abstract-We propose an image-based virtual fitting system to reproduce the appearance of fitting during online shopping for garments. Inputs are whole-body images of a fashion model and the customer. We create a garment image by cutting out the garment portion from the image of the fashion model. If the garment images were naïvely superimposed, the fitting result would look strange mainly because the shape of the garment will not match the body shape of the customer. In this paper, we therefore propose a method of reshaping the garment image based on human body shapes of the fashion model and the customer to make the fitting result more realistic. The body shape is automatically estimated from the contours of the human body, and can easily be retouched if necessary. The fitting result is refined further by automatic color correction with reference to the facial regions and a method of retouching parts that protrude from the rear of the garment image. We verified the effectiveness of our system through a user test.

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Sensitivity-optimized rigging for example-based real-time clothing synthesis

Cited by 62 publications

References 39 publications

Learning‐Based Animation of Clothing for Virtual Try‐On

Learning‐Based Animation of Clothing for Virtual Try‐On

SimulCap : Single-View Human Performance Capture With Cloth Simulation

Image-Based Virtual Fitting System with Garment Image Reshaping

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