Accurate Face Rig Approximation with Deep Differential Subspace Reconstruction
June 02, 2020 Β· Declared Dead Β· π ACM Transactions on Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Steven L. Song, Weiqi Shi, Michael Reed
arXiv ID
2006.01746
Category
cs.GR: Graphics
Cross-listed
cs.LG
Citations
27
Venue
ACM Transactions on Graphics
Last Checked
5 months ago
Abstract
To be suitable for film-quality animation, rigs for character deformation must fulfill a broad set of requirements. They must be able to create highly stylized deformation, allow a wide variety of controls to permit artistic freedom, and accurately reflect the design intent. Facial deformation is especially challenging due to its nonlinearity with respect to the animation controls and its additional precision requirements, which often leads to highly complex face rigs that are not generalizable to other characters. This lack of generality creates a need for approximation methods that encode the deformation in simpler structures. We propose a rig approximation method that addresses these issues by learning localized shape information in differential coordinates and, separately, a subspace for mesh reconstruction. The use of differential coordinates produces a smooth distribution of errors in the resulting deformed surface, while the learned subspace provides constraints that reduce the low frequency error in the reconstruction. Our method can reconstruct both face and body deformations with high fidelity and does not require a set of well-posed animation examples, as we demonstrate with a variety of production characters.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Graphics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Deep Bilateral Learning for Real-Time Image Enhancement
R.I.P.
π»
Ghosted
Animating Human Athletics
R.I.P.
π»
Ghosted
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
R.I.P.
π»
Ghosted
Shape Transformation Using Variational Implicit Functions
R.I.P.
π»
Ghosted
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted