7 research outputs found
ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
We present ClothCombo, a pipeline to drape arbitrary combinations of clothes
on 3D human models with varying body shapes and poses. While existing
learning-based approaches for draping clothes have shown promising results,
multi-layered clothing remains challenging as it is non-trivial to model
inter-cloth interaction. To this end, our method utilizes a GNN-based network
to efficiently model the interaction between clothes in different layers, thus
enabling multi-layered clothing. Specifically, we first create feature
embedding for each cloth using a topology-agnostic network. Then, the draping
network deforms all clothes to fit the target body shape and pose without
considering inter-cloth interaction. Lastly, the untangling network predicts
the per-vertex displacements in a way that resolves interpenetration between
clothes. In experiments, the proposed model demonstrates strong performance in
complex multi-layered scenarios. Being agnostic to cloth topology, our method
can be readily used for layered virtual try-on of real clothes in diverse poses
and combinations of clothes
Deep Detail Enhancement for Any Garment
Creating fine garment details requires significant efforts and huge
computational resources. In contrast, a coarse shape may be easy to acquire in
many scenarios (e.g., via low-resolution physically-based simulation, linear
blend skinning driven by skeletal motion, portable scanners). In this paper, we
show how to enhance, in a data-driven manner, rich yet plausible details
starting from a coarse garment geometry. Once the parameterization of the
garment is given, we formulate the task as a style transfer problem over the
space of associated normal maps. In order to facilitate generalization across
garment types and character motions, we introduce a patch-based formulation,
that produces high-resolution details by matching a Gram matrix based style
loss, to hallucinate geometric details (i.e., wrinkle density and shape). We
extensively evaluate our method on a variety of production scenarios and show
that our method is simple, light-weight, efficient, and generalizes across
underlying garment types, sewing patterns, and body motion.Comment: 12 page
Motion Guided Deep Dynamic 3D Garments
Realistic dynamic garments on animated characters have many AR/VR
applications. While authoring such dynamic garment geometry is still a
challenging task, data-driven simulation provides an attractive alternative,
especially if it can be controlled simply using the motion of the underlying
character. In this work, we focus on motion guided dynamic 3D garments,
especially for loose garments. In a data-driven setup, we first learn a
generative space of plausible garment geometries. Then, we learn a mapping to
this space to capture the motion dependent dynamic deformations, conditioned on
the previous state of the garment as well as its relative position with respect
to the underlying body. Technically, we model garment dynamics, driven using
the input character motion, by predicting per-frame local displacements in a
canonical state of the garment that is enriched with frame-dependent skinning
weights to bring the garment to the global space. We resolve any remaining
per-frame collisions by predicting residual local displacements. The resultant
garment geometry is used as history to enable iterative rollout prediction. We
demonstrate plausible generalization to unseen body shapes and motion inputs,
and show improvements over multiple state-of-the-art alternatives.Comment: 11 page
Unilaterally Incompressible Skinning
Skinning was initially devised for computing the skin of a character deformed through a skeleton; but it is now also commonly used for deforming tight garments at a very cheap cost. However, unlike skin which may easily compress and stretch, tight cloth strongly resists compression: inside bending regions such as the interior of an elbow, cloth does not shrink but instead buckles, causing interesting folds and wrinkles which are completely missed by skinning methods. Our goal is to extend traditional skinning in order to capture such folding patterns automatically, without sacrificing efficiency. The key of our model is to replace the usual skinning formula — derived from, e.g., Linear Blend Skinning or Dual Quaternions — with a complementarity constraint, making an automatic switch between, on the one hand, classical skinning in zones prone to stretching, and on the other hand, a quasi-isometric scheme in zones prone to compression. Moreover, our method provides some useful handles to the user for directing the type of folds created, such as the fold density or the overall shape of a given fold. Our results show that our method can generate similar complexity of folds compared to full cloth simulation, while retaining interactivity of skinning approaches and offering intuitive user control