Lightweight wrinkle synthesis for 3D facial modeling and animation

Li, Jun; Xu, Weiwei; Cheng, Zhi-Quan; Xu, Kai; Klein, Reinhard

doi:10.1016/j.cad.2014.08.016

Cited by 18 publications

(4 citation statements)

References 28 publications

(39 reference statements)

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“…They can obtain realistic details, but the details are not specific to the target face. Multi-identity local models [30,45,14,18] can be built from high-resolution scan datasets. However, they only model patch-based local detail displacements, effective for detail reconstruction but cannot synthesize detail animation.…”

Section: Related Workmentioning

confidence: 99%

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Ling¹,

Wang²,

Lu³

et al. 2022

Preprint

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Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on largescale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structureaware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/ gerwang/facial-detail-manipulation.

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Section: Related Workmentioning

confidence: 99%

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Ling¹,

Wang²,

Lu³

et al. 2022

Preprint

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“…High‐quality multi‐view approaches obtain high‐quality fine‐scale surface reconstructions [bba*07, FP09, BHPS10, BHB*11, FJA*14] based on a controlled capture setup at slow off‐line frame rates. Based on a corpus of high‐quality 3D scans, generative wrinkle formation models can be learned [HYZ*12, BBB*14, LXC*15a,CBZB15] as an alternative to shape‐from‐shading. Highresolution static face scans can be dynamically combined based on video input to model wrinkle formation [FJA*14].…”

Section: Advanced Face Models and Personalized Rigsmentioning

confidence: 99%

State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

Zollhöfer

Thies

Garrido

et al. 2018

Computer Graphics Forum

305

152

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The computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input. Over the past years rapid progress has been made, which led to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB‐D camera. The range of applications is vast and steadily growing as these technologies are further improving in speed, accuracy, and ease of use. Motivated by this rapid progress, this state‐of‐the‐art report summarizes recent trends in monocular facial performance capture and discusses its applications, which range from performance‐based animation to real‐time facial reenactment. We focus our discussion on methods where the central task is to recover and track a three dimensional model of the human face using optimization‐based reconstruction algorithms. We provide an in‐depth overview of the underlying concepts of real‐world image formation, and we discuss common assumptions and simplifications that make these algorithms practical. In addition, we extensively cover the priors that are used to better constrain the under‐constrained monocular reconstruction problem, and discuss the optimization techniques that are employed to recover dense, photo‐geometric 3D face models from monocular 2D data. Finally, we discuss a variety of use cases for the reviewed algorithms in the context of motion capture, facial animation, as well as image and video editing.

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“…These methods include the effects of specularity and are able to generate displacement maps. Large wrinkles can also be captured by low cost 3D cameras when they are enhanced using an existing scan [15]. Graham et al [16] uses a method to take small samples of the skin's microstructure and synthesizes a more detailed topology based on an existing normal map.…”

Section: A Related Workmentioning

confidence: 99%

Example-Based Skin Wrinkle Displacement Maps

Vanderfeesten

Bikker

2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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We present an algorithm for generating procedural displacement maps for wrinkle patterns measured from photographs or scans. These displacement maps can contain wrinkle patterns that appear at the meso-and microscale, and are modeled using several spatially varying parameters such as the size, shape and distribution of each individual skin wrinkle. We present an algorithm to measure the parameters of skin wrinkle patterns, and show how to adapt the measured parameters to generate displacement maps with similar properties for 3D models other than the one measured. Lastly, we evaluate the quality of the generated maps by comparing them to maps acquired by scanning human skin.

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Lightweight wrinkle synthesis for 3D facial modeling and animation

Cited by 18 publications

References 28 publications

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

Example-Based Skin Wrinkle Displacement Maps

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