Inferring Unseen Views of People

Chen, Chao-Yeh; Grauman, Kristen

doi:10.1109/cvpr.2014.258

Cited by 21 publications

(12 citation statements)

References 43 publications

(77 reference statements)

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“…A simplified version of unseen view prediction is predicting HOG descriptors [9] instead of images. Chen et al [10] pose the problem as tensor completion. Su et al [11] find object parts similar to those of a given object in a large dataset of 3D models and interpolate between the desired views of these.…”

Section: Related Workmentioning

confidence: 99%

Multi-view 3D Models from Single Images with a Convolutional Network

Tatarchenko

Dosovitskiy

Brox

2016

Lecture Notes in Computer Science

327

334

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Abstract. We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object. Concretely, the network can predict an RGB image and a depth map of the object as seen from an arbitrary view. Several of these depth maps fused together give a full point cloud of the object. The point cloud can in turn be transformed into a surface mesh. The network is trained on renderings of synthetic 3D models of cars and chairs. It successfully deals with objects on cluttered background and generates reasonable predictions for real images of cars.

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Section: Related Workmentioning

confidence: 99%

Multi-view 3D Models from Single Images with a Convolutional Network

Tatarchenko

Dosovitskiy

Brox

2016

Lecture Notes in Computer Science

327

334

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“…Here, v i indicates the ID of the vertex where the i-th image of the object instance is observed. For instance, {v i } 20 i=1 in Candidate #2 is {1, 5,2,6,3,7,4,8,13,15,14,16,17,18,19,20,9,11,10, 12} ( Fig. 15 (b)).…”

Section: Sensitivity To Pre-defined Views Assumptionmentioning

confidence: 99%

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

Kanezaki

Matsushita

Nishida

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

427

256

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We propose a Convolutional Neural Network (CNN)based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on

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“…Viewpoint estimation is significant in its role in improving object classification. Better performance was achieved on face identification [49], human action classification [7], and image retrieval [38] by generating unseen views after observing a single view. These methods "imagine" the appearance of objects' unobserved profiles, which is innately more uncertain than using real observations.…”

Section: Related Workmentioning

confidence: 99%

RotationNet for Joint Object Categorization and Unsupervised Pose Estimation from Multi-View Images

Kanezaki

Matsushita

Nishida

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet uses only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10-and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves comparable performance to the state-of-the-art methods on an object pose estimation dataset. Furthermore, our object ranking method based on classification by RotationNet achieved the first prize in two tracks of the 3D Shape Retrieval Contest (SHREC) 2017. Finally, we demonstrate the performance of real-world applications of RotationNet trained with our newly created multi-view image dataset using a moving USB camera.

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Inferring Unseen Views of People

Abstract: We pose unseen view synthesis as a probabilistic tensor completion problem.

Cited by 21 publications

References 43 publications

Multi-view 3D Models from Single Images with a Convolutional Network

Multi-view 3D Models from Single Images with a Convolutional Network

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

RotationNet for Joint Object Categorization and Unsupervised Pose Estimation from Multi-View Images

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