To build a virtual city that are close to a real city, a large number of various types of human models need to be created. To reduce the cost of acquiring models, this paper proposes a method to reconstruct 3D human meshes from single images captured using a normal camera.

Apr 30, 2014 · The second row shows the smoothed contour of a segmented human body, convex points on the contour, COG, and the intersection points of the major and minor axes with the contour. The third row shows the located 2D significant points for each image.

Mar 8, 2022 · The second row is where most of the tools in MakeHuman are stored. You will typically move from left to right along these tabs as you create a new model. The third row changes based on the context from the second row. Next we will create a very quick human being model for demonstration. Create a Human

We present a method to reconstruct the body geome-try of a person by aligning the skinned multi-person linear (SMPL) model to an unconstrained human image.

Dec 4, 2024 · We leverage a single-view human model, pre-trained on large-scale 2D human data, to learn a transferred multi-view body representation. We integrate multimodal facial features from 2D and 3D priors to provide structure-accurate and identity-preserving information, enhancing face representation.

Dec 1, 2024 · In order to increase the reliability of the pose representation and bring the 3D information into the generation process, we propose to use the 2D projection of the pose-driven 3D human model as the input of the flow prediction …

Dec 7, 2024 · To address this limitation, we propose a novel 3D-guided training method that leverages 3D loss to improve 2D pose estimation. Additionally, we introduce a multi-model training method to ensure robust generalization across various 3D lifting networks.

Oct 1, 2018 · In this paper, we present a practicable method to estimate individual 3D human model in a low cost multi-view realtime 3D human motion capture system.

Mar 26, 2024 · In this paper, we present InterGen, an effective diffusion-based approach that enables layman users to customize high-quality two-person interaction motions, with only text guidance. We first contribute a multimodal dataset, named InterHuman.

In this paper we propose an end-to-end learning pipeline to construct full body, statistical human shape and pose models capable of actuating facial expressions, as well as body and hand motion.