Easy acquisition and real‐time animation of facial wrinkles

Abstract

Facial animation details like wrinkles or bulges are very useful for the analysis and the interpretation of facial emotions and expressions. However, outfitting a virtual face with expression details for real-time applications is a difficult task. In this paper, we propose a mono-camera acquisition technique of facial animation details and a technique which add a wrinkle map layer (fine-scale animation) to a skinning layer (large-scale animation) for real-time rendering of a virtual 3D face. The acquisition is based on ratio image computed from two pictures of a same face, with and without expression. The real-time dynamic wrinkles technique is based on a small set of reference poses. These two methods offer an easy and lowcost way to capture facial animation details and use it for real-time facial animation.

Passing an Enhanced Turing Test – Interacting with Lifelike Computer Representations of Specific Individuals

“…Applied wrinkle map shader in Figure 21 There are a few recent studies that deployed this method to depict more natural dynamics of facial wrinkle generation. Dutreve proposed a dynamic wrinkle control via skeletal animation poses as it controls facial animation with bones [14]. Another example is a region-based mask to activate a certain area of the wrinkle map when an application triggers an expression associated with the wrinkle region [48].…”

Data-driven Fingertip Appearance for Interactive Hand Simulation

“…In an anatomically accurate approach, geometric features on the back of hand can be driven by tendon simulations [Sueda et al 2008]. Alternatively, Huang et al [2011] capture and model pose dependent wrinkles on hands, while another popular approach is to build wrinkle texture maps into the character rig to provide these details without the cost of geometric modeling [Oat 2007;Dutreve et al 2011]. Similar to pose space deformation [Lewis et al 2000] and shape by example [Sloan et al 2001], the EigenSkin technique interpolates shapes within a memory efficient basis computed with principal component analysis [Kry et al 2002].…”

Automatic generation of facial wrinkles according to expression changes

“…Whereas, we have to collect suitable ratio images to create desired facial expressions beforehand. Dutreve et al [2011] developed a method for automatically applying the optimum normal map obtained from a ratio image to a facial model with the animation of expression. The problem is, however, that the method is effective only against a common skeleton-based model.…”