Search Results for author: Kripasindhu Sarkar

Found 13 papers, 1 papers with code

Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision

no code implementations CVPR 2022 Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Diogo Luvizon, Christian Theobalt

Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision.

Egocentric Pose Estimation

EgoRenderer: Rendering Human Avatars from Egocentric Camera Images

no code implementations ICCV 2021 Tao Hu, Kripasindhu Sarkar, Lingjie Liu, Matthias Zwicker, Christian Theobalt

We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network.

Texture Synthesis Translation

Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control

no code implementations3 Jun 2021 Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, Christian Theobalt

To address this problem, we utilize a coarse body model as the proxy to unwarp the surrounding 3D space into a canonical pose.

Estimating Egocentric 3D Human Pose in Global Space

no code implementations ICCV 2021 Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Christian Theobalt

Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.

3D Human Pose Estimation

HumanGAN: A Generative Model of Humans Images

no code implementations11 Mar 2021 Kripasindhu Sarkar, Lingjie Liu, Vladislav Golyanik, Christian Theobalt

We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style.

Pose Transfer

Style and Pose Control for Image Synthesis of Humans from a Single Monocular View

no code implementations22 Feb 2021 Kripasindhu Sarkar, Vladislav Golyanik, Lingjie Liu, Christian Theobalt

Photo-realistic re-rendering of a human from a single image with explicit control over body pose, shape and appearance enables a wide range of applications, such as human appearance transfer, virtual try-on, motion imitation, and novel view synthesis.

Image Generation Novel View Synthesis +1

Neural Re-Rendering of Humans from a Single Image

no code implementations ECCV 2020 Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, Christian Theobalt

Human re-rendering from a single image is a starkly under-constrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealistic distortions of the body parts and garments, or implausible changes of the texture.

Translation

Pose-Guided Human Animation from a Single Image in the Wild

no code implementations CVPR 2021 Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik, Kripasindhu Sarkar, Hyun Soo Park, Christian Theobalt

We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses.

Pose Transfer

Structured 2D Representation of 3D Data for Shape Processing

no code implementations25 Mar 2019 Kripasindhu Sarkar, Elizabeth Mathews, Didier Stricker

We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative and geometric tasks on 3D shapes.

Classification General Classification

Learning Quadrangulated Patches For 3D Shape Processing

no code implementations25 Mar 2019 Kripasindhu Sarkar, Kiran varanasi, Didier Stricker

We propose a system for surface completion and inpainting of 3D shapes using generative models, learnt on local patches.

Denoising

Learning quadrangulated patches for 3D shape parameterization and completion

no code implementations20 Sep 2017 Kripasindhu Sarkar, Kiran varanasi, Didier Stricker

By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape.

Denoising Dictionary Learning

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