It is extremely challenging to create an animatable clothed human avatar from RGB videos,
especially for loose clothes due to the difficulties in motion modeling. To address this …

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …

Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of
a category and generating dense correspondences among shapes. With DIF, a 3D shape is …

Precise representations of 3D faces are beneficial to various computer vision and graphics
applications. Due to the data discretization and model linearity however, it remains …

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier
morphable face models it not only captures identity-specific geometry, texture, and …

Abstract Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape
representation. To capture geometry details, current methods usually learn DIF using local …

This paper introduces a novel framework called DT-Net for 3D mesh reconstruction and
generation via Disentangled Topology. Beyond previous works, we learn a topology-aware …

While neural representations for static 3D shapes are widely studied, representations for
deformable surfaces are limited to be template-dependent or to lack efficiency. We introduce …

Creating animatable avatars from static scans requires the modeling of clothing
deformations in different poses. Existing learning-based methods typically add pose …