30 research outputs found
Example-based wrinkle synthesis for clothing animation
This paper describes a method for animating the appearance of clothing, such as pants or a shirt, that fits closely to a figure's body. Compared to flowing cloth, such as loose dresses or capes, these types of garments involve nearly continuous collision contact and small wrinkles, that can be troublesome for traditional cloth simulation methods. Based on the observation that the wrinkles in closefitting clothing behave in a predominantly kinematic fashion, we have developed an example-based wrinkle synthesis technique. Our method drives wrinkle generation from the pose of the figure's kinematic skeleton. This approach allows high quality clothing wrinkles to be combined with a coarse cloth simulation that computes the global and dynamic aspects of the clothing motion. While the combined results do not exactly match a high-resolution reference simulation, they do capture many of the characteristic fine-scale features and wrinkles. Further, the combined system runs at interactive rates, making it suitable for applications where high-resolution offline simulations would not be a viable option. The wrinkle synthesis method uses a precomputed database built by simulating the high-resolution clothing as the articulated figure is moved over a range of poses. In principle, the space of poses is exponential in the total number of degrees of freedom; however clothing wrinkles are primarily affected by the nearest joints, allowing each joint to be processed independently. During synthesis, mesh interpolation is used to consider the influence of multiple joints, and combined with a coarse simulation to produce the final results at interactive rates
Example-based wrinkle synthesis for clothing animation
This paper describes a method for animating the appearance of clothing, such as pants or a shirt, that fits closely to a figure's body. Compared to flowing cloth, such as loose dresses or capes, these types of garments involve nearly continuous collision contact and small wrinkles, that can be troublesome for traditional cloth simulation methods. Based on the observation that the wrinkles in closefitting clothing behave in a predominantly kinematic fashion, we have developed an example-based wrinkle synthesis technique. Our method drives wrinkle generation from the pose of the figure's kinematic skeleton. This approach allows high quality clothing wrinkles to be combined with a coarse cloth simulation that computes the global and dynamic aspects of the clothing motion. While the combined results do not exactly match a high-resolution reference simulation, they do capture many of the characteristic fine-scale features and wrinkles. Further, the combined system runs at interactive rates, making it suitable for applications where high-resolution offline simulations would not be a viable option. The wrinkle synthesis method uses a precomputed database built by simulating the high-resolution clothing as the articulated figure is moved over a range of poses. In principle, the space of poses is exponential in the total number of degrees of freedom; however clothing wrinkles are primarily affected by the nearest joints, allowing each joint to be processed independently. During synthesis, mesh interpolation is used to consider the influence of multiple joints, and combined with a coarse simulation to produce the final results at interactive rates
Spatially Adaptive Cloth Regression with Implicit Neural Representations
The accurate representation of fine-detailed cloth wrinkles poses significant
challenges in computer graphics. The inherently non-uniform structure of cloth
wrinkles mandates the employment of intricate discretization strategies, which
are frequently characterized by high computational demands and complex
methodologies. Addressing this, the research introduced in this paper
elucidates a novel anisotropic cloth regression technique that capitalizes on
the potential of implicit neural representations of surfaces. Our first core
contribution is an innovative mesh-free sampling approach, crafted to reduce
the reliance on traditional mesh structures, thereby offering greater
flexibility and accuracy in capturing fine cloth details. Our second
contribution is a novel adversarial training scheme, which is designed
meticulously to strike a harmonious balance between the sampling and simulation
objectives. The adversarial approach ensures that the wrinkles are represented
with high fidelity, while also maintaining computational efficiency. Our
results showcase through various cloth-object interaction scenarios that our
method, given the same memory constraints, consistently surpasses traditional
discrete representations, particularly when modelling highly-detailed localized
wrinkles.Comment: 16 pages, 13 figure
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
While models of 3D clothing learned from real data exist, no method can
predict clothing deformation as a function of garment size. In this paper, we
introduce SizerNet to predict 3D clothing conditioned on human body shape and
garment size parameters, and ParserNet to infer garment meshes and shape under
clothing with personal details in a single pass from an input mesh. SizerNet
allows to estimate and visualize the dressing effect of a garment in various
sizes, and ParserNet allows to edit clothing of an input mesh directly,
removing the need for scan segmentation, which is a challenging problem in
itself. To learn these models, we introduce the SIZER dataset of clothing size
variation which includes different subjects wearing casual clothing items
in various sizes, totaling to approximately 2000 scans. This dataset includes
the scans, registrations to the SMPL model, scans segmented in clothing parts,
garment category and size labels. Our experiments show better parsing accuracy
and size prediction than baseline methods trained on SIZER. The code, model and
dataset will be released for research purposes.Comment: European Conference on Computer Vision 202
Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation
Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent intrinsic parameters (e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a differentiable simulator to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%