Pose estimation and body tracking using an artificial neural network
C. Öztireli, P. Chandran, M. Gross
Patent Office: US, Patent Number: 10970849
Posts by Collection
patents
Appearance synthesis of digital faces
P. Chandran, T. Beeler, D. Bradley,
Patent Office: US, Patent Number: 11257276
Semantic deep face models
P. Chandran, T. Beeler, D. Bradley,
Patent Office: US, Patent Number: 11276231
Adaptive convolutions in neural networks
P. Chandran, D. Bradley, P. Gotardo, G. Zoss
Patent Office: US, Application Number: 17223577
TECHNIQUES FOR ENHANCING SKIN RENDERS USING NEURAL NETWORK PROJECTION FOR RENDERING COMPLETION
D. Bradley, P. Chandran, P. Gotardo, J Riviere, S. Winberg, G. Zoss
Patent Office: US, Application Number: 17536777
TECHNIQUES FOR RE-AGING FACES IN IMAGES AND VIDEO FRAMES
G Zoss, D E Bradley, P Chandran, P F Gotardo, E Sifakis
Patent Office: US, Application Number: 17536777
TECHNIQUES FOR RE-AGING FACES IN IMAGES AND VIDEO FRAMES
G Zoss, D E Bradley, P Chandran, P F Gotardo, E Sifakis
Patent Office: US, Application Number: 17536777
Generating a facial-hair-free mesh of a subject
S Winberg, DE Bradley, P Chandran, PFU Gotardo, G Zoss
Patent Office: US, Application Number: 18102480
Shape and appearance reconstruction with deep geometric refinement
DE Bradley, P Chandran, PFU Gotardo, C Otto, A Serifi, G Zoss
Patent Office: US, Application Number: 17669053
Techniques for improved lighting models for appearance capture
PFU Gotardo, DE Bradley, G Zoss, J Riviere, P Chandran, Xu Yingyan
Patent Office: US, Application Number: 18081593
Techniques for improved lighting models for appearance capture
PFU Gotardo, DE Bradley, G Zoss, J Riviere, P Chandran, Xu Yingyan
Patent Office: US, Application Number: 18081597
Synthesizing sequences of 3d geometries for movement-based performance
DE Bradley, P Chandran, PFU Gotardo, G Zoss
Patent Office: US, Application Number: 17526608
Facial animation retargeting using a patch blend-shape solver
P Chandran, LF Ciccone, DE Bradley
Patent Office: US, Application Number: 17586449
Techniques for monocular face capture using a perceptual shape loss
DE Bradley, P Chandran, PFU Gotardo, C Otto, G Zoss
Patent Office: US, Application Number: 18599021
Data-driven physics-based facial animation retargeting
DE Bradley, P Chandran, ED Sifakis, B Solenthaler, PFU Gotardo, L Yang, G Zoss
Patent Office: US, Application Number: 18421429
Relightable neural radiance field model
DE Bradley, P Chandran, PFU Gotardo, XU Yingyan, G Zoss
Patent Office: US, Application Number: 18505009
Anatomically constrained implicit shape models
G Zoss, P Chandran
Patent Office: US, Application Number: 18780259
Shape reconstruction and editing using anatomically constrained implicit shape models
G Zoss, P Chandran
Patent Office: US, Application Number: 18780264
Flexible 3d landmark detection
DE Bradley, P Chandran, G Zoss
Patent Office: US, Application Number: 18906555
Face micro detail recovery via patch scanning, interpolation, and style transfer
DE Bradley, SK Weiss, P Chandran, G Zoss, JR Stanhope
Patent Office: US, Application Number: 18906639
Graph simulation for facial micro features with dynamic animation
DE Bradley, P Chandran, PFU Gotardo, SK Weiss, G Zoss
Patent Office: US, Application Number: 18628602
Graph simulation for facial micro features with dynamic animation
DE Bradley, P Chandran, SK Weiss, G Zoss
Patent Office: US, Application Number: 18628611
Joint image normalization and landmark detection
DE Bradley, P Chandran, G Zoss
Patent Office: US, Application Number: 18906545
Local identity-aware facial rig generation
P Chandran, G Zoss, DE Bradley, JE Klintberg, PFU Gotardo
Patent Office: US, Application Number: 18786337
Face reconstruction using a mesh convolution network
DE Bradley P Chandran S FOTI PFU Gotardo G Zoss
Patent Office: US, Application Number: 17697774
Synthesizing sequences of images for movement-based performance
DE Bradley, P Chandran, PFU Gotardo, G Zoss
Patent Office: US, Application Number: 17526647
Query deformation for landmark annotation correction
DE Bradley, P Chandran, G Zoss
Patent Office: US, Application Number: 18906575
Transformer-based shape models
DE Bradley, P Chandran, PFU Gotardo, G Zoss
Patent Office: US, Application Number: 17675713
portfolio
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publications
Segmentation and grading of diabetic retinopathic exudates using error-boost feature selection method
Published in 2011 World Congress on Information and Communication Technologies, 2011
This paper proposes a method to segment the exudates and lesions in retinal fundus images and classify using selective brightness feature. [Paper]
Road tracking using particle filters for Advanced Driver Assistance Systems
Published in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014
Road segmentation and tracking is of prime importance in Advanced Driver Assistance Systems (ADAS) to either assist autonomous navigation or provide useful information to drivers operating semi-autonomous vehicles. The work reported herein describes a novel algorithm based on particle filters for segmenting and tracking the edges of roads in real world scenarios. [Paper]
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today.
Project Page
Published in 3D International Conference on 3D Vision (3DV), 2020
We present a method for nonlinear 3D face modeling using neural architectures.
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Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
We propose Adaptive convolutions; a generic extension of AdaIN, which allows for the simultaneous transfer of both statistical and structural styles in real time.
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Published in ACM SIGGRAPH Asia, 2021
We propose to combine incomplete, high-quality renderings showing only facial skin with recent methods for neural rendering of faces, in order to automatically and seamlessly create photo-realistic full-head portrait renders from captured data without the need for artist intervention.
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Published in Eurographics, 2022
We present a new nonlinear parametric 3D shape model based on transformer architectures.
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Published in Eurographics, 2022
We compare the results obtained with a state-of-the-art appearance capture method, with and without our proposed improvements to the lighting model.
Project Page
Published in Siggraph, 2022
We present a new method for high-fidelity offline facial performance retargeting that is neither expensive nor artifact-prone.
Project Page
Published in Siggraph, 2022
We demonstrate the proposed capture pipeline on a variety of different facial hair styles and lengths, ranging from sparse and short to dense full-beards.
Project Page
Published in Siggraph, 2022
We demonstrate how MoRF is a strong new step towards 3D morphable neural head modeling.
Project Page
Published in ACM/Eurographics Symposium on Computer Animation, 2022
We propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non-rigid motion during a performance.
Project Page
Published in Pacific Graphics, 2022
We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with identity-specific decoders.
Project Page
Published in Siggraph Asia, 2022
We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable expressions, viewpoints, and lighting conditions.
Project Page
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
We propose the first facial landmark detection network that can predict continuous, unlimited landmarks, allowing to specify the number and location of the desired landmarks at inference time.
Project Page
Published in Eurographics Symposium on Geometry Processing, 2023
We present a novel graph-based simulation approach for generating micro wrinkle geometry on human skin, which can easily scale up to the micro-meter range and millions of wrinkles.
Project Page
Published in International Conference on Computer Vision (ICCV), 2023
In this paper, we target the application scenario of capturing high-fidelity assets for neural relighting in controlled studio conditions, but without requiring a dense light stage. Instead, we leverage a small number of area lights commonly used in photogrammetry.
Project Page
Published in Pacific Graphics, 2023
In this work, we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image. It is widely known that shading provides a strong indicator for 3D shape in the human visual system.
Project Page
Published in Siggraph Asia, 2023
We propose a new face model based on a data-driven implicit neural physics model that can be driven by both expression and style separately. At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities.
Project Page
Published in Eurographics, 2024
In this work we aim to overcome the gap between synthetic simulation and real skin scanning, by proposing a method that can be applied to large skin regions (e.g. an entire face) with the controllability of simulation and the organic look of real micro details.
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Published in Eurographics, 2024
We present a new method to animate the dynamic motion of skin micro wrinkles under facial expression deformation.
Project Page
Published in Computer Vision and Pattern Recognition (CVPR), 2024
In this work, we present a novel use case for such implicit representations in the context of learning anatomically constrained face models.
Project Page
Published in Computer Vision and Pattern Recognition (CVPR), 2024
In this work, we simultaneously tackle both the motion and illumination problem, proposing a new method for relightable and animatable neural heads.
Project Page
Published in Computer Graphics Forum, 2024
In this work, we examine 3 important issues in the practical use of state-of-the-art facial landmark detectors and show how a combination of specific architectural modifications can directly improve their accuracy and temporal stability.
Project Page
Published in Siggraph, 2024
In this work, we aim to make physics-based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3D face dataset. Once trained, our model can be quickly fit to any unseen identity and produce a ready-to-animate physical face model automatically.
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Published in Europen Conference on Computer Vision (ECCV), 2024
We introduce Spline-based Transformers, a new class of transformer models that do not require position encoding.
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Published in Eurographics, 2025
We address the practical problem of generating facial blendshapes and reference animations for a new 3D character in production environments.
Project Page
Published in SIGGRAPH, 2025
In this work, we propose to couple locally-defined facial expressions with 3D Gaussian splatting to enable creating ultra-high fidelity, expressive and photorealistic head avatars.
Project Page
Published in ICCV, 2025
In this work, we present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment.
Project Page
Published in Workshop on Human-Interactive Generation and Editing, 2025
In this work, we propose to jointly learn the visual appearance and depth of faces simultaneously in a diffusion-based portrait image generator. Our method embraces the end-to-end diffusion paradigm and introduces a new architecture suitable for learning this joint distribution, consisting of a reference network for target identity and a channel expanded diffusion backbone.
Project Page
Published in Shape Modeling International, 2025
In this work, we present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals.
Project Pagetalks
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teaching
Course, Siggraph Asia 2023, Sydney, 2023
This course goes over the history of face models used in computer animation. The course covers a wide variety of models starting from linear blendshape models that provide intuitive artist control to more recent and powerful nonlinear neural shape models. link to course material
Course, Eurographics 2024, Limassol, 2024
This course is a revised extension of the course I presented in Siggraph Asia 2023, with added material about physics based facial animation from Dr. Lingchen Yang.
link to course material