Image and video synthesis has become a blooming topic in computer vision and machine
learning communities along with the developments of deep generative models, due to its …

Various applications require high-fidelity and artifact-free 3D human reconstructions.
However current implicit function-based methods inevitably produce artifacts while existing …

In this paper, we introduce Human-LRM, a single-stage feed-forward Large Reconstruction
Model designed to predict human Neural Radiance Fields (NeRF) from a single image. Our …

Leveraging pretrained 2D diffusion models and score distillation sampling (SDS), recent
methods have shown promising results for text-to-3D avatar generation. However …

This paper presents the DiffusionRegPose a novel approach to multi-person pose
estimation that converts a one-stage end-to-end keypoint regression model into a diffusion …

The reconstruction of high-quality 3D clothed humans from monocular images or videos has
gained popularity in recent years due to its significant practical applications. While several …

We present a novel framework for reconstructing animatable human avatars from multiple
images, termed CanonicalFusion. Our central concept involves integrating individual …

Many application fields including virtual reality and movie production demand reconstructing
high-quality digital human avatars from monocular videos and real-time rendering. However …

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from
a single image has long been a challenging task. While deep learning networks enable us …

3D human shape reconstruction under severe occlusion due to human-object or human-
human interaction is a challenging problem. Parametric models ie, SMPL (-X), which are …