フェイシャルリグの修正:カメラ方向による顔の変形の自動化 - アニつく改!!
前回の年賀ムービーを経て、フェイシャルリグの修正中です。 今回は、カメラ方向による顔の変形リグについて。 顔を捉えるカメラの方向によって、目や口の位置をずらしたり変形させて、 立体(3D)としての嘘をつくことで、2Dのような絵に寄せていました。 ですが、ポーズごとに、実際にその変形を加えていくのは、なかなかしんどい為、 ある程度自動化できないかと思って、つくってみました。 構造は、たいしたものではありませんが、カメラにルックアットしているボーンの角度変化を利用して、FFDにスキンモーフをかけて、ジオメトリにあてています。 その結果がこちらです。 カメラ方向による顔の変形
Bringing Portraits to Life
Given a single image (top row), our method automatically generates photo-realistic videos that express various emotions. We use driving videos of a different subject and mimic the expressiveness of the subject in the driving video. Representative frames from the videos are displayed above. Abstract We present a technique to automatically animate a still portrait, making it possible for the subject
Dynamixyz
Markerless Facial Motion Capture Solution for achieversAs of July 1, 2021, the whole team is thrilled to announce that Dynamixyz is part of Take-Two Interactive Software, Inc. We’re very excited to join development capabilities and participate in Take-Two’s mission to remain the most creative, innovative and efficient entertainment company. It’s been a great ride since 2010, and we have finished w
Speech and Facial Animation at SIGGRAPH 2017 -- comparison of technical papers - YouTube
Comparison of three technical papers presented at SIGGRAPH 2017 in the "Speech and Facial Animation" session. http://s2017.siggraph.org/technical-papers/sessions/speech-and-facial-animation Tero Karras, Timo Aila, Samuli Laine, Antti Herva, and Jaakko Lehtinen. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion. ACM Trans. Graph. 36, 4, Article 94 (July 2017). http://r
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion | Research
Publications Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the var
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks - YouTube
In Proceedings of SCA'17, Los Angeles, CA, USA, July 28-30, 2017 http://research.nvidia.com/publication/facial-performance-capture-deep-neural-networks Samuli Laine (NVIDIA) Tero Karras (NVIDIA) Timo Aila (NVIDIA) Antti Herva (Remedy Entertainment) Shunsuke Saito (Pinscreen, University of Southern California) Ronald Yu (Pinscreen, University of Southern California) Hao Li (USC Institute for Crea
Modeling Vellus Facial Hair from Asperity Scattering Silhouettes
Modeling Vellus Facial Hair from Asperity Scattering Silhouettes SIGGRAPH 2017 Conference Proceedings Chloe LeGendre Loc Hyunh Shanhe Wang Paul Debevec USC Institute for Creative Technologies Video renderings of three backlit subjects with vellus hair modeled using our approach and added to their 3D facial scans. (Best viewed at 4K resolution.) Abstract We present a technique for modeling the vell
The tech of Crytek’s Ryse: Son of Rome
The tech of Crytek’s Ryse: Son of Rome Posted by Ian Failes ON March 20, 2014 When Microsoft announced the Xbox One in May 2013, Ryse: Son of Rome was confirmed to be one of the first titles available for the next-gen console. For Crytek, that provided a chance to develop a game with characters and environments seen in a level of fidelity they had not previously tackled. “In the past,” Crytek art
faceshift
faceshift is accurate, effortless, and affordable markerless facial performance capture. faceshift uses depth cameras such as Microsoft's Kinect to animate rigs in real time. faceshift works seamlessly for fast facial expressions, head motions, and difficult environments. news Open BetaWe started the open beta, get your free faceshift version and send us your feedback, such that we can focus on wh
Mova : Home
“CONTOUR is a breakthrough technology for digital effects production. Performances can now be captured in 3D as they are performed, eliminating much of the post-production work required in the past... It isn't just capturing dots in space anymore; it's actual live-action volumetric capture.” —Ed Ulbrich, Senior Vice President and Executive Producer, Digital Domain, whose credits include Academy Aw
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カメラ方向による顔の変形
Animatable Facial Reflectance Fields
Facial Reflectance Field Demo
Modeling Vellus Facial Hair from Asperity Scattering Silhouettes
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