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 define and study a new Cloth2Body problem which has a goalof generating 3D human body meshes from a 2D clothing image. Unlike theexisting human mesh recovery problem, Cloth2Body needs to address new andemerging challenges raised by the partial observation of the input and the highdiversity of the output. Indeed, there are three specific challenges. First,how to locate and pose human bodies into the clothes. Second, how toeffectively estimate body shapes out of various clothing types. Finally, how togenerate diverse and plausible results from a 2D clothing image. To this end,we propose an end-to-end framework that can accurately estimate 3D body meshparameterized by pose and shape from a 2D clothing image. Along this line, wefirst utilize Kinematics-aware Pose Estimation to estimate body poseparameters. 3D skeleton is employed as a proxy followed by an inversekinematics module to boost the estimation accuracy. We additionally design anadaptive depth trick to align the re-projected 3D mesh better with 2D clothingimage by disentangling the effects of object size and camera extrinsic. Next,we propose Physics-informed Shape Estimation to estimate body shape parameters.3D shape parameters are predicted based on partial body measurements estimatedfrom RGB image, which not only improves pixel-wise human-cloth alignment, butalso enables flexible user editing. Finally, we design Evolution-based posegeneration method, a skeleton transplanting method inspired by geneticalgorithms to generate diverse reasonable poses during inference. As shown byexperimental results on both synthetic and real-world data, the proposedframework achieves state-of-the-art performance and can effectively recovernatural and diverse 3D body meshes from 2D images that align well withclothing.

Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing