16 research outputs found
PERGAMO: Personalized 3D Garments from Monocular Video
Clothing plays a fundamental role in digital humans. Current approaches to
animate 3D garments are mostly based on realistic physics simulation, however,
they typically suffer from two main issues: high computational run-time cost,
which hinders their development; and simulation-to-real gap, which impedes the
synthesis of specific real-world cloth samples. To circumvent both issues we
propose PERGAMO, a data-driven approach to learn a deformable model for 3D
garments from monocular images. To this end, we first introduce a novel method
to reconstruct the 3D geometry of garments from a single image, and use it to
build a dataset of clothing from monocular videos. We use these 3D
reconstructions to train a regression model that accurately predicts how the
garment deforms as a function of the underlying body pose. We show that our
method is capable of producing garment animations that match the real-world
behaviour, and generalizes to unseen body motions extracted from motion capture
dataset.Comment: Published at Computer Graphics Forum (Proc. of ACM/SIGGRAPH SCA),
2022. Project website http://mslab.es/projects/PERGAMO
SPA: Sparse Photorealistic Animation using a single RGB-D camera
Photorealistic animation is a desirable technique for computer games and movie production. We propose a new method to synthesize plausible videos of human actors with new motions using a single cheap RGB-D camera. A small database is captured in a usual office environment, which happens only once for synthesizing different motions. We propose a markerless performance capture method using sparse deformation to obtain the geometry and pose of the actor for each time instance in the database. Then, we synthesize an animation video of the actor performing the new motion that is defined by the user. An adaptive model-guided texture synthesis method based on weighted low-rank matrix completion is proposed to be less sensitive to noise and outliers, which enables us to easily create photorealistic animation videos with new motions that are different from the motions in the database. Experimental results on the public dataset and our captured dataset have verified the effectiveness of the proposed method