Multi-view multi-person 3D pose estimation aims to localize 3D skeleton joints for each person instance in a scene from multi-view camera inputs. It is a fundamental task that benefits many real-world applications (such assurveillance, sportscast, gaming and mixed reality) and ismainly tackled byreconstruction-based [6,14,4]andvolumetric [40]approaches inpreviousliterature, as showninFig.1(a)and(b).

The proposed MLP decoder aggregates information from different layers, andthus combining both local attention and global attention to render powerful representations.

Compared toprevious works, our approach can learn landmarks that are more flexible in terms of capturing large changes in viewpoint.

The shared part contains information for both domains. The exclusive parts, on the other hand, contain only factors of variation that are particular to each domain.

However,most existing studies concentrate onthetext-to-image synthesis [11-14], which generates images from text descriptions without the original image.

Recent progress hasdemonstrated thatsuchmeta-learning methods may exceed scalable human-invented architectures on image classification tasks.

To tackle such an issue as well as promote the quality of object attention, we introduce asimple yet effectiveSelfErasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions.

Such biases are known asrepresentation bias [38].

Semantic segmentation is a fundamental problem in computer vision.

Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart.