Reconstructing dynamic objects from monocular videos is a severely underconstrained and
challenging problem and recent work has approached it in various directions. However …

Despite recent research advancements in reconstructing clothed humans from a single
image, accurately restoring the “unseen regions” with high-level details remains an …

We present CARTO, a novel approach for reconstructing multiple articulated objects from a
single stereo RGB observation. We use implicit object-centric representations and learn a …

We propose a novel 3D morphable model for complete human heads based on hybrid
neural fields. At the core of our model lies a neural parametric representation that …

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

Obtaining personalized 3D animatable avatars from a monocular camera has several real
world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very …

In this paper, we propose a novel hybrid representation and end-to-end trainable network
architecture to model fully editable and customizable neural avatars. At the core of our work …

Garments are important to humans. A visual system that can estimate and track the complete
garment pose can be useful for many downstream tasks and real-world applications. In this …

Dense correspondence between humans carries powerful semantic information that can be
utilized to solve fundamental problems for full-body understanding such as in-the-wild …

We propose LaplacianFusion, a novel approach that reconstructs detailed and controllable
3D clothed-human body shapes from an input depth or 3D point cloud sequence. The key …