Learning best views of 3D shapes from sketch contour

Zhao, Long; Liang, Shuang; Jia, Jinyuan; Wei, Yichen

doi:10.1007/s00371-015-1091-1

Cited by 18 publications

(17 citation statements)

References 32 publications

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“…Contour rendering. Recent studies [27,28] present a hybrid line rendering method to generate 2D contour maps for 3D shapes and obtain good performance. In this paper, we adopt this approach which combines predefined exterior silhouettes, occluding contours, suggestive contours [29] and shape boundaries to generate the final contour map from a given view point for further editing.…”

Section: Contour-based Face Model Refinementmentioning

confidence: 99%

Cartoonish sketch-based face editing in videos using identity deformation transfer

Zhao

Han

Peng

et al. 2019

Computers & Graphics

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A B S T R A C TWe address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth. This task is formulated as a 3D face model reconstruction and deformation problem. We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame. At the same time, user's editing intention is recognized from input sketches as a set of facial modifications. Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions. After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity. Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.

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Section: Contour-based Face Model Refinementmentioning

confidence: 99%

Cartoonish sketch-based face editing in videos using identity deformation transfer

Zhao

Han

Peng

et al. 2019

Computers & Graphics

Self Cite

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“…Another approach conducted a simple, statistical analysis of a high volume of internet photos in order to directly determine the canonical views [HO05, MW12]. In addition, [ZLJW15] demonstrated that the semantic viewpoints can be trained even by hand drawings.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the selected salient views do not always correspond to our common knowledge of the objects. The other approaches [FCODS08, MS09, LZH12, ZLJW15] study some high‐level measurements in seeking meaningful information, but generalization of the hand‐designed features is not always straightforward for more sophisticated cases.…”

Section: Introductionmentioning

confidence: 99%

Category‐Specific Salient View Selection via Deep Convolutional Neural Networks

Kim

Tai

Lee

et al. 2017

Computer Graphics Forum

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In this paper, we present a new framework to determine up front orientations and detect salient views of 3D models. The salient viewpoint to human preferences is the most informative projection with correct upright orientation. Our method utilizes two Convolutional Neural Network (CNN) architectures to encode category‐specific information learnt from a large number of 3D shapes and 2D images on the web. Using the first CNN model with 3D voxel data, we generate a CNN shape feature to decide natural upright orientation of 3D objects. Once a 3D model is upright‐aligned, the front projection and salient views are scored by category recognition using the second CNN model. The second CNN is trained over popular photo collections from internet users. In order to model comfortable viewing angles of 3D models, a category‐dependent prior is also learnt from the users. Our approach effectively combines category‐specific scores and classical evaluations to produce a data‐driven viewpoint saliency map. The best viewpoints from the method are quantitatively and qualitatively validated with more than 100 objects from 20 categories. Our thumbnail images of 3D models are the most favoured among those from different approaches.

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“…This can eliminate the negative interference of the bad viewpoint image for our retrieval results. This method is adopted by Zhao et al [25] to acquire the best-view images of a model. Here, we only want to get the good view images, not best-view image.…”

Section: Svm Classifiermentioning

confidence: 99%

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Zhou¹,

Jia²

2017

dtcse

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Abstract. With the rapid development of 3D technology, the demand to use and retrieve the 3D model is becoming more and more urgent. In this case, it is more and more import and necessary that sketch-based 3D model retrieval. The user provides a hand-drawn sketch, then the system can be provided the possible list of models, then the user can select the need model from this list. In this paper, it's proposed a framework of using SVM classification methods and K-means cluster algorithm. In preprocess part; the adaptive thinning algorithm is used to process the input sketch. In the offline part, the model is projected to multi-view images, and then SVM classifier has been used to classify these images. Moreover, we extract the images features, and use K-means algorithm to cluster these features. Finally, the feature dictionary will be acquired. In online process part, it only needs to extract the input sketch feature, and computes the distance between dictionary and their features. Besides, the experiment is realized to verify the feasibility of the approach. Finally, it was compared with other's approaches; the result shows that the approach is viable and robustness.

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Learning best views of 3D shapes from sketch contour

Cited by 18 publications

References 32 publications

Cartoonish sketch-based face editing in videos using identity deformation transfer

Cartoonish sketch-based face editing in videos using identity deformation transfer

Category‐Specific Salient View Selection via Deep Convolutional Neural Networks

SVM: Sketch-based 3D Retrieval Application Using Classification Method

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