Search Results for author: Vladislav Golyanik

Found 65 papers, 9 papers with code

HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization

no code implementations ECCV 2020 Neng Qian, Jiayi Wang, Franziska Mueller, Florian Bernard, Vladislav Golyanik, Christian Theobalt

3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality.

Neural Rendering

Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints

no code implementations ECCV 2020 Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, Antonio Agudo, Christian Theobalt

We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks.

3D Shape Reconstruction

NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory

no code implementations24 Aug 2023 Navami Kairanda, Marc Habermann, Christian Theobalt, Vladislav Golyanik

Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature.

ROAM: Robust and Object-aware Motion Generation using Neural Pose Descriptors

no code implementations24 Aug 2023 Wanyue Zhang, Rishabh Dabral, Thomas Leimkühler, Vladislav Golyanik, Marc Habermann, Christian Theobalt

Given an unseen object and a reference pose-object pair, we optimise for the object-aware pose that is closest in the feature space to the reference pose.

Motion Synthesis

VINECS: Video-based Neural Character Skinning

no code implementations3 Jul 2023 Zhouyingcheng Liao, Vladislav Golyanik, Marc Habermann, Christian Theobalt

However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time.

AvatarStudio: Text-driven Editing of 3D Dynamic Human Head Avatars

no code implementations1 Jun 2023 Mohit Mendiratta, Xingang Pan, Mohamed Elgharib, Kartik Teotia, Mallikarjun B R, Ayush Tewari, Vladislav Golyanik, Adam Kortylewski, Christian Theobalt

Our method edits the full head in a canonical space, and then propagates these edits to remaining time steps via a pretrained deformation network.

EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

no code implementations2 May 2023 Xinyu Yi, Yuxiao Zhou, Marc Habermann, Vladislav Golyanik, Shaohua Pan, Christian Theobalt, Feng Xu

We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.

Simultaneous Localization and Mapping

Plotting Behind the Scenes: Towards Learnable Game Engines

no code implementations23 Mar 2023 Willi Menapace, Aliaksandr Siarohin, Stéphane Lathuilière, Panos Achlioptas, Vladislav Golyanik, Elisa Ricci, Sergey Tulyakov

The key to learning such game AI is the exploitation of a large and diverse text corpus, collected in this work, describing detailed actions in a game and used to train our animation model.


Scene-Aware 3D Multi-Human Motion Capture from a Single Camera

1 code implementation12 Jan 2023 Diogo Luvizon, Marc Habermann, Vladislav Golyanik, Adam Kortylewski, Christian Theobalt

In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera.

MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

no code implementations CVPR 2023 Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, Christian Theobalt

Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality.

Denoising Motion Synthesis

Fast Non-Rigid Radiance Fields from Monocularized Data

no code implementations2 Dec 2022 Moritz Kappel, Vladislav Golyanik, Susana Castillo, Christian Theobalt, Marcus Magnor

3D reconstruction and novel view synthesis of dynamic scenes from collections of single views recently gained increased attention.

3D Reconstruction Novel View Synthesis

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

no code implementations27 Oct 2022 Edith Tretschk, Navami Kairanda, Mallikarjun B R, Rishabh Dabral, Adam Kortylewski, Bernhard Egger, Marc Habermann, Pascal Fua, Christian Theobalt, Vladislav Golyanik

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.

3D Reconstruction

HiFECap: Monocular High-Fidelity and Expressive Capture of Human Performances

no code implementations11 Oct 2022 Yue Jiang, Marc Habermann, Vladislav Golyanik, Christian Theobalt

Furthermore, we show that HiFECap outperforms the state-of-the-art human performance capture approaches qualitatively and quantitatively while for the first time capturing all aspects of the human.

Vocal Bursts Intensity Prediction

MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes

1 code implementation17 Aug 2022 Zhi Li, Soshi Shimada, Bernt Schiele, Christian Theobalt, Vladislav Golyanik

3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem.

3D Human Pose Estimation

EventNeRF: Neural Radiance Fields from a Single Colour Event Camera

no code implementations CVPR 2023 Viktor Rudnev, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik

Asynchronously operating event cameras find many applications due to their high dynamic range, vanishingly low motion blur, low latency and low data bandwidth.

3D Reconstruction Novel View Synthesis +1

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance

no code implementations11 May 2022 Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, Christian Theobalt

Marker-less monocular 3D human motion capture (MoCap) with scene interactions is a challenging research topic relevant for extended reality, robotics and virtual avatar generation.

Quantum Motion Segmentation

no code implementations24 Mar 2022 Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, Vladislav Golyanik

Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.

Motion Segmentation

Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization

no code implementations23 Mar 2022 Alp Yurtsever, Tolga Birdal, Vladislav Golyanik

We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA).

Graph Matching

φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

no code implementations22 Mar 2022 Navami Kairanda, Edith Tretschk, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik

In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties.

3D Reconstruction Physical Simulations

f-SfT: Shape-From-Template With a Physics-Based Deformation Model

no code implementations CVPR 2022 Navami Kairanda, Edith Tretschk, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik

In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties.

3D Reconstruction Physical Simulations

NeRF for Outdoor Scene Relighting

no code implementations9 Dec 2021 Viktor Rudnev, Mohamed Elgharib, William Smith, Lingjie Liu, Vladislav Golyanik, Christian Theobalt

Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reflectance and illumination.

Adiabatic Quantum Graph Matching with Permutation Matrix Constraints

no code implementations8 Jul 2021 Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt, Michael Moeller

In this work, we address such problems with emerging quantum computing technology and propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware.

Graph Matching

HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks

no code implementations2 Jul 2021 Jameel Malik, Soshi Shimada, Ahmed Elhayek, Sk Aziz Ali, Christian Theobalt, Vladislav Golyanik, Didier Stricker

To address the limitations of the existing methods, we develop HandVoxNet++, i. e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner.

3D Hand Pose Estimation

Fast Simultaneous Gravitational Alignment of Multiple Point Sets

no code implementations21 Jun 2021 Vladislav Golyanik, Soshi Shimada, Christian Theobalt

The problem of simultaneous rigid alignment of multiple unordered point sets which is unbiased towards any of the inputs has recently attracted increasing interest, and several reliable methods have been newly proposed.

Q-Match: Iterative Shape Matching via Quantum Annealing

no code implementations ICCV 2021 Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller

Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density.

Neural Monocular 3D Human Motion Capture with Physical Awareness

no code implementations3 May 2021 Soshi Shimada, Vladislav Golyanik, Weipeng Xu, Patrick Pérez, Christian Theobalt

We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios.

3D Pose Estimation

Differentiable Event Stream Simulator for Non-Rigid 3D Tracking

no code implementations30 Apr 2021 Jalees Nehvi, Vladislav Golyanik, Franziska Mueller, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt

This paper introduces the first differentiable simulator of event streams, i. e., streams of asynchronous brightness change signals recorded by event cameras.

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

Quantum Permutation Synchronization

no code implementations CVPR 2021 Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas Guibas

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision.

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.


Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

2 code implementations ICCV 2021 Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, Christian Theobalt

We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e. g. a `bullet-time' video effect.

Novel View Synthesis Video Editing

High-Fidelity Neural Human Motion Transfer from Monocular Video

1 code implementation CVPR 2021 Moritz Kappel, Vladislav Golyanik, Mohamed Elgharib, Jann-Ole Henningson, Hans-Peter Seidel, Susana Castillo, Christian Theobalt, Marcus Magnor

We address these limitations for the first time in the literature and present a new framework which performs high-fidelity and temporally-consistent human motion transfer with natural pose-dependent non-rigid deformations, for several types of loose garments.

Image Generation Vocal Bursts Intensity Prediction

EventHands: Real-Time Neural 3D Hand Pose Estimation from an Event Stream

1 code implementation ICCV 2021 Viktor Rudnev, Vladislav Golyanik, Jiayi Wang, Hans-Peter Seidel, Franziska Mueller, Mohamed Elgharib, Christian Theobalt

Due to the different data modality of event cameras compared to classical cameras, existing methods cannot be directly applied to and re-trained for event streams.

3D Hand Pose Estimation

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

Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations

no code implementations28 Sep 2020 Sk Aziz Ali, Kerem Kahraman, Christian Theobalt, Didier Stricker, Vladislav Golyanik

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA).

PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time

no code implementations20 Aug 2020 Soshi Shimada, Vladislav Golyanik, Weipeng Xu, Christian Theobalt

We, therefore, present PhysCap, the first algorithm for physically plausible, real-time and marker-less human 3D motion capture with a single colour camera at 25 fps.

A Quantum Computational Approach to Correspondence Problems on Point Sets

no code implementations CVPR 2020 Vladislav Golyanik, Christian Theobalt

Modern adiabatic quantum computers (AQC) are already used to solve difficult combinatorial optimisation problems in various domains of science.

Accelerated Gravitational Point Set Alignment With Altered Physical Laws

no code implementations ICCV 2019 Vladislav Golyanik, Christian Theobalt, Didier Stricker

This work describes Barnes-Hut Rigid Gravitational Approach (BH-RGA) -- a new rigid point set registration method relying on principles of particle dynamics.

Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity

no code implementations5 Sep 2019 Vladislav Golyanik, André Jonas, Didier Stricker, Christian Theobalt

The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios.

DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies

no code implementations24 Jul 2019 Soshi Shimada, Vladislav Golyanik, Edgar Tretschk, Didier Stricker, Christian Theobalt

We introduce a supervised-learning framework for non-rigid point set alignment of a new kind - Displacements on Voxels Networks (DispVoxNets) - which abstracts away from the point set representation and regresses 3D displacement fields on regularly sampled proxy 3D voxel grids.

Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data

no code implementations12 May 2019 Onorina Kovalenko, Vladislav Golyanik, Jameel Malik, Ahmed Elhayek, Didier Stricker

SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences.

3D Reconstruction

IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction

no code implementations27 Apr 2019 Soshi Shimada, Vladislav Golyanik, Christian Theobalt, Didier Stricker

The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates.

3D Reconstruction

HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model

no code implementations27 Mar 2018 Vladislav Golyanik, Soshi Shimada, Kiran varanasi, Didier Stricker

Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision.

3D Reconstruction

Scalable Dense Monocular Surface Reconstruction

no code implementations17 Oct 2017 Mohammad Dawud Ansari, Vladislav Golyanik, Didier Stricker

This paper reports on a novel template-free monocular non-rigid surface reconstruction approach.

Surface Reconstruction

Multiframe Scene Flow with Piecewise Rigid Motion

no code implementations5 Oct 2017 Vladislav Golyanik, Kihwan Kim, Robert Maier, Matthias Nießner, Didier Stricker, Jan Kautz

We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences.

Scene Flow Estimation

Gravitational Approach for Point Set Registration

no code implementations CVPR 2016 Vladislav Golyanik, Sk Aziz Ali, Didier Stricker

In this paper a new astrodynamics inspired rigid point set registration algorithm is introduced -- the Gravitational Approach (GA).

Cannot find the paper you are looking for? You can Submit a new open access paper.