Prashanth Chandran

Prashanth Chandran

Research Scientist

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

patents

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.

Project Page

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.

Project Page

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.

Project Page

Published in Eurographics, 2022

We present a new nonlinear parametric 3D shape model based on transformer architectures.

Project Page

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.

Project Page

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.

Project Page

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.

Project Page

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 Page

Published in Computer Vision and Pattern Recognition (CVPR), 2026

We present CUBE (Control-based Unified B-spline Encoding), a new geometric representation for human faces that combines B-spline volumes with learned features, and demonstrate its use as a decoder for 3D scan registration and monocular 3D face reconstruction.

Project Page

Download here

Published in SIGGRAPH, 2026

From calibrated multi-view images, SHELLS reconstructs 18k-vertex 3D heads in 0.08 seconds. It aggregates DinoV2 features via projective surface-aware feature sampling, allowing a transformer to predict dense semantic meshes 3.5x faster with 88% less GPU memory than state-of-the-art methods.

Project Page

Download here

Published in arXiv, 2026

Parametric models of the human head are essential tools in computer vision, graphics, and generative AI. We introduce the Generative aNthropometric Model (GNM), a comprehensive 3D head model encompassing the head, face, neck, eyeballs, teeth, and tongue, built on high-resolution 3D scans and high-quality artist-made samples.

Project Page Code

Download here

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