Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

Recent advances in neural rendering have improved both training and rendering times by
orders of magnitude. While these methods demonstrate state-of-the-art quality and speed …

In this study, we introduce a methodology for human image animation by leveraging a 3D
human parametric model within a latent diffusion framework to enhance shape alignment …

Current methods for learning realistic and animatable 3D clothed avatars need either posed
3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn …

Photorealistic rendering and reposing of humans is important for enabling augmented reality
experiences. We propose a novel framework to reconstruct the human and the scene that …

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 …

Unconditional human image generation is an important task in vision and graphics, enabling
various applications in the creative industry. Existing studies in this field mainly focus on …

We propose SelfRecon, a clothed human body reconstruction method that combines implicit
and explicit representations to recover space-time coherent geometries from a monocular …