556 research outputs found
Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting
We propose a novel self-supervised framework for retargeting
non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and
poses, enabling 3D virtual try-on (VTON). Existing self-supervised 3D
retargeting methods only support parametric and canonical garments, which can
only be draped over parametric body, e.g. SMPL. To facilitate the
non-parametric garments and body, we propose a novel method that introduces
Isomap Embedding based correspondences matching between the garment and the
human body to get a coarse alignment between the two meshes. We perform neural
refinement of the coarse alignment in a self-supervised setting. Further, we
leverage a Laplacian detail integration method for preserving the inherent
details of the input garment. For evaluating our 3D non-parametric garment
retargeting framework, we propose a dataset of 255 real-world garments with
realistic noise and topological deformations. The dataset contains unique
garments worn by 15 different subjects in 5 distinctive poses, captured using a
multi-view RGBD capture setup. We show superior retargeting quality on
non-parametric garments and human avatars over existing state-of-the-art
methods, acting as the first-ever baseline on the proposed dataset for
non-parametric 3D garment retargeting
Sketching-out virtual humans: From 2d storyboarding to immediate 3d character animation
Virtual beings are playing a remarkable role in today’s public entertainment, while ordinary users are still treated as audiences due to the lack of appropriate expertise, equipment, and computer skills. In this paper, we present a fast and intuitive storyboarding interface, which enables users to sketch-out 3D virtual humans, 2D/3D animations, and character intercommunication. We devised an intuitive “stick figurefleshing-outskin mapping” graphical animation pipeline, which realises the whole process of key framing, 3D pose reconstruction, virtual human modelling, motion path/timing control, and the final animation synthesis by almost pure 2D sketching. A “creative model-based method” is developed, which emulates a human perception process, to generate the 3D human bodies of variational sizes, shapes, and fat distributions. Meanwhile, our current system also supports the sketch-based crowd animation and the storyboarding of the 3D multiple character intercommunication. This system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling
Synthetic data has emerged as a promising source for 3D human research as it
offers low-cost access to large-scale human datasets. To advance the diversity
and annotation quality of human models, we introduce a new synthetic dataset,
SynBody, with three appealing features: 1) a clothed parametric human model
that can generate a diverse range of subjects; 2) the layered human
representation that naturally offers high-quality 3D annotations to support
multiple tasks; 3) a scalable system for producing realistic data to facilitate
real-world tasks. The dataset comprises 1.2M images with corresponding accurate
3D annotations, covering 10,000 human body models, 1,187 actions, and various
viewpoints. The dataset includes two subsets for human pose and shape
estimation as well as human neural rendering. Extensive experiments on SynBody
indicate that it substantially enhances both SMPL and SMPL-X estimation.
Furthermore, the incorporation of layered annotations offers a valuable
training resource for investigating the Human Neural Radiance Fields (NeRF).Comment: Accepted by ICCV 2023. Project webpage: https://synbody.github.io
Methods for reducing visual discomfort in stereoscopic 3D: A review
This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This work presents 4DHumanOutfit, a new dataset of densely sampled
spatio-temporal 4D human motion data of different actors, outfits and motions.
The dataset is designed to contain different actors wearing different outfits
while performing different motions in each outfit. In this way, the dataset can
be seen as a cube of data containing 4D motion sequences along 3 axes with
identity, outfit and motion. This rich dataset has numerous potential
applications for the processing and creation of digital humans, e.g. augmented
reality, avatar creation and virtual try on. 4DHumanOutfit is released for
research purposes at https://kinovis.inria.fr/4dhumanoutfit/. In addition to
image data and 4D reconstructions, the dataset includes reference solutions for
each axis. We present independent baselines along each axis that demonstrate
the value of these reference solutions for evaluation tasks
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