Search Results for author: Anastasia Tkach

Found 4 papers, 1 papers with code

EgoPoseFormer: A Simple Baseline for Egocentric 3D Human Pose Estimation

no code implementations26 Mar 2024 Chenhongyi Yang, Anastasia Tkach, Shreyas Hampali, Linguang Zhang, Elliot J. Crowley, Cem Keskin

We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25. 5mm (21% improvement) compared to the best existing method with only 60. 7% model parameters and 36. 4% FLOPs.

Egocentric Pose Estimation

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

no code implementations12 Nov 2018 Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello

We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.

Denoising Super-Resolution

Low-Dimensionality Calibration Through Local Anisotropic Scaling for Robust Hand Model Personalization

1 code implementation ICCV 2017 Edoardo Remelli, Anastasia Tkach, Andrea Tagliasacchi, Mark Pauly

We present a robust algorithm for personalizing a sphere-mesh tracking model to a user from a collection of depth measurements.

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