English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we consider a novel problem of reconstructing a 3D humanavatar from multiple unconstrained frames, independent of assumptions on cameracalibration, capture space, and constrained actions. The problem should beaddressed by a framework that takes multiple unconstrained images as inputs,and generates a shape-with-skinning avatar in the canonical space, finished inone feed-forward pass. To this end, we present 3D Avatar Reconstruction in thewild (ARwild), which first reconstructs the implicit skinning fields in amulti-level manner, by which the image features from multiple images arealigned and integrated to estimate a pixel-aligned implicit function thatrepresents the clothed shape. To enable the training and testing of the newframework, we contribute a large-scale dataset, MVP-Human (Multi-View andmulti-Pose 3D Human), which contains 400 subjects, each of which has 15 scansin different poses and 8-view images for each pose, providing 6,000 3D scansand 48,000 images in total. Overall, benefits from the specific networkarchitecture and the diverse data, the trained model enables 3D avatarreconstruction from unconstrained frames and achieves state-of-the-artperformance.

MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained Frames