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Vladislav Golyanik

52 accepted papers

2026

$\boldsymbol{\partial^\infty}$-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids

Navami Kairanda, Shanthika Naik, Marc Habermann, Avinash Sharma, Christian Theobalt, Vladislav Golyanik
ICLR 2026poster

We present a novel differentiable grid-based representation for efficiently solving differential equations (DEs). Widely used architectures for neural solvers, such as sinusoidal neural networks, are coordinate-based MLPs that are, both, computationally intensive and slow to train. Although grid-bas…

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2026

E-3DPSM: A State Machine for Event-based Egocentric 3D Human Pose Estimation

Mayur Deshmukh, Hiroyasu Akada, Helge Rhodin, Christian Theobalt, Vladislav Golyanik
CVPR 2026

Event cameras offer multiple advantages in monocular egocentric 3D human pose estimation from head-mounted devices, such as millisecond temporal resolution, high dynamic range, and negligible motion blur. Existing methods effectively leverage these properties, but suffer from low 3D estimation accur

Cited by 0Source ↗Scholar ↗
2026

Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views

Kunwar Maheep Singh, Jianchun Chen, Vladislav Golyanik, Stephan J. Garbin, Thabo Beeler, Rishabh Dabral +2
CVPR 2026

We present _Relightable Holoported Characters_ (RHC), a novel person-specific method for free-view rendering and relighting of full-body and highly dynamic humans solely observed from sparse-view RGB videos at inference. In contrast to classical one-light-at-a-time (OLAT)-based human relighting, our

Cited by 0Source ↗Scholar ↗
2026

SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokens

Anindita Ghosh, Vladislav Golyanik, Taku Komura, Philipp Slusallek, Christian Theobalt, Rishabh Dabral
CVPR 2026

Synthesizing text-driven 3D human motion within realistic scenes requires learning both semantic intent ("walk to the couch") and physical feasibility (e.g., avoiding collisions). Current methods use generative frameworks that simultaneously learn high-level planning and low-level contact reasoning,

Cited by 0Source ↗code ↗Scholar ↗
2026

Splat the Net: Radiance Fields with Splattable Neural Primitives

xilong zhou, Bao-Huy Nguyen, Loïc Magne, Vladislav Golyanik, Thomas Leimkuehler, Christian Theobalt
ICLR 2026poster

Radiance fields have emerged as a predominant representation for modeling 3D scene appearance. Neural formulations such as Neural Radiance Fields provide high expressivity but require costly ray marching for rendering, whereas primitive-based methods such as 3D Gaussian Splatting offer real-time eff…

Cited by 0Source ↗code ↗Scholar ↗
2025

Attention (as Discrete-Time Markov) Chains

Yotam Erel, Olaf Dünkel, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, Amit Haim Bermano
NeurIPS 2025poster

We introduce a new interpretation of the attention matrix as a discrete-time Markov chain. Our interpretation sheds light on common operations involving attention scores such as selection, summation, and averaging in a unified framework. It further extends them by considering indirect attention, pro…

Cited by 0Source ↗Scholar ↗
2025

BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects

Wanyue Zhang, Rishabh Dabral, Vladislav Golyanik, Vasileios Choutas, Eduardo Alvarado, Thabo Beeler +2
CVPR 2025poster

We present BimArt, a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects. Unlike prior works, we do not rely on a reference grasp, a coarse hand trajectory, or separate modes for grasping and articulating. To achieve this, we first generate distance-base…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

Bring Your Rear Cameras for Egocentric 3D Human Pose Estimation

Hiroyasu Akada, Jian Wang, Vladislav Golyanik, Christian Theobalt
ICCV 2025poster

Egocentric 3D human pose estimation has been actively studied using cameras installed in front of a head-mounted device (HMD). While frontal placement is the optimal and the only option for some tasks, such as hand tracking, it remains unclear if the same holds for full-body tracking due to self-occ…

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2025

DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image

Qingxuan Wu, Zhiyang Dou, Sirui Xu, Soshi Shimada, Chen Wang, Zhengming Yu +8
ICLR 2025poster

Reconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem from self-occlusions during single-view hand-face interactions, diverse spatial relationships between hands and face, co…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2025

Denoising Functional Maps: Diffusion Models for Shape Correspondence

Aleksei Zhuravlev, Zorah Lähner, Vladislav Golyanik
CVPR 2025poster

Estimating correspondences between pairs of deformable shapes remains a challenging problem. Despite substantial progress, existing methods lack broad generalization capabilities and require category-specific training data. To address these limitations, we propose a fundamentally new approach to sha…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis

Timo Teufel, Pulkit Gera, Xilong Zhou, Umar Iqbal, Pramod Rao, Jan Kautz +2
ICCV 2025poster

Simultaneous relighting and novel-view rendering of digital human representations is an important yet challenging task with numerous applications. However, progress in this area has been significantly limited due to the lack of publicly available, high-quality datasets, especially for full-body huma…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers

Natacha Kuete Meli, Vladislav Golyanik, Marcel Seelbach Benkner, Michael Moeller
CVPR 2025highlight

There is growing interest in solving computer vision problems such as mesh or point set alignment using Adiabatic Quantum Computing (AQC). Unfortunately, modern experimental AQC devices such as D-Wave only support Quadratic Unconstrained Binary Optimisation (QUBO) problems, which severely limits the…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields

Navami Kairanda, Marc Habermann, Shanthika Naik, Christian Theobalt, Vladislav Golyanik
CVPR 2025poster

3D reconstruction of highly deformable surfaces (e.g. cloths) from monocular RGB videos is a challenging problem, and no solution provides a consistent and accurate recovery of fine-grained surface details. To account for the ill-posed nature of the setting, existing methods use deformation models w…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

3D Human Pose Perception from Egocentric Stereo Videos

Hiroyasu Akada, Jian Wang, Vladislav Golyanik, Christian Theobalt
CVPR 2024highlight

While head-mounted devices are becoming more compact they provide egocentric views with significant self-occlusions of the device user. Hence existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work we propose a new transformer-based framework to improv…

Cited by 19Source ↗PDF ↗Scholar ↗
2024

EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams

Christen Millerdurai, Hiroyasu Akada, Jian Wang, Diogo Luvizon, Christian Theobalt, Vladislav Golyanik
CVPR 2024poster

Monocular egocentric 3D human motion capture is a challenging and actively researched problem. Existing methods use synchronously operating visual sensors (e.g. RGB cameras) and often fail under low lighting and fast motions which can be restricting in many applications involving head-mounted device…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2024

Holoported Characters: Real-time Free-viewpoint Rendering of Humans from Sparse RGB Cameras

Ashwath Shetty, Marc Habermann, Guoxing Sun, Diogo Luvizon, Vladislav Golyanik, Christian Theobalt
CVPR 2024poster

We present the first approach to render highly realistic free-viewpoint videos of a human actor in general apparel from sparse multi-view recording to display in real-time at an unprecedented 4K resolution. At inference our method only requires four camera views of the moving actor and the respectiv…

Cited by 9Source ↗PDF ↗Scholar ↗
2024

NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory

Navami Kairanda, Marc Habermann, Christian Theobalt, Vladislav Golyanik
NeurIPS 2024poster

Despite existing 3D cloth simulators producing realistic results, they predominantly operate on discrete surface representations (e.g. points and meshes) with a fixed spatial resolution, which often leads to large memory consumption and resolution-dependent simulations. Moreover, back-propagating gr…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions

Anindita Ghosh*, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek
ECCV 2024poster

"Current approaches for 3D human motion synthesis generate high-quality animations of digital humans performing a wide variety of actions and gestures. However, a notable technological gap exists in addressing the complex dynamics of multi-human interactions within this paradigm. In this work, we pr…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2024

Relightable Neural Actor with Intrinsic Decomposition and Pose Control

Diogo Carbonera Luvizon*, Vladislav Golyanik, Adam Kortylewski, Marc Habermann, Christian Theobalt
ECCV 2024poster

"Creating a controllable and relightable digital avatar from multi-view video with fixed illumination is a very challenging problem since humans are highly articulated, creating pose-dependent appearance effects, and skin as well as clothing require space-varying BRDF modeling. Existing works on cre…

Cited by 4Source ↗PDF ↗Scholar ↗
2024

VINECS: Video-based Neural Character Skinning

Zhouyingcheng Liao, Vladislav Golyanik, Marc Habermann, Christian Theobalt
CVPR 2024poster

Rigging and skinning clothed human avatars is a challenging task and traditionally requires a lot of manual work and expertise. Recent methods addressing it either generalize across different characters or focus on capturing the dynamics of a single character observed under different pose configurat…

Cited by 3Source ↗PDF ↗Scholar ↗
2023

CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes

Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt +1
CVPR 2023poster

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, NP-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum…

Cited by 15Source ↗PDF ↗Scholar ↗
2023

EventNeRF: Neural Radiance Fields From a Single Colour Event Camera

Viktor Rudnev, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik
CVPR 2023poster

Asynchronously operating event cameras find many applications due to their high dynamic range, vanishingly low motion blur, low latency and low data bandwidth. The field saw remarkable progress during the last few years, and existing event-based 3D reconstruction approaches recover sparse point clou…

Cited by 91Source ↗PDF ↗code ↗Scholar ↗
2023

Mofusion: A Framework for Denoising-Diffusion-Based Motion Synthesis

Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, Christian Theobalt
CVPR 2023highlight

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional…

Cited by 187Source ↗PDF ↗Scholar ↗
2023

QuAnt: Quantum Annealing with Learnt Couplings

Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik
ICLR 2023top-25%

Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computer vision remains challenging and currently requires problem-specific analytic…

Cited by 5Source ↗PDF ↗Scholar ↗
2023

Quantum Multi-Model Fitting

Matteo Farina, Luca Magri, Willi Menapace, Elisa Ricci, Vladislav Golyanik, Federica Arrigoni
CVPR 2023highlight

Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the l…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2023

Self-Supervised Pre-Training With Masked Shape Prediction for 3D Scene Understanding

Li Jiang, Zetong Yang, Shaoshuai Shi, Vladislav Golyanik, Dengxin Dai, Bernt Schiele
CVPR 2023poster

Masked signal modeling has greatly advanced self-supervised pre-training for language and 2D images. However, it is still not fully explored in 3D scene understanding. Thus, this paper introduces Masked Shape Prediction (MSP), a new framework to conduct masked signal modeling in 3D scenes. MSP uses…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2022

HULC: 3D HUman Motion Capture with Pose Manifold SampLing and Dense Contact Guidance

Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, Christian Theobalt
ECCV 2022poster

"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. Due to the inherent depth ambiguity of monocular settings, 3D motions captured with existing methods often contain sev…

Cited by 31Source ↗PDF ↗Scholar ↗
2022

NeRF for Outdoor Scene Relighting

Viktor Rudnev, Mohamed Elgharib, William Smith, Lingjie Liu, Vladislav Golyanik, Christian Theobalt
ECCV 2022poster

"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. A delicate manipulation of the lighting can then be performed while keeping the scene albedo and…

Cited by 150Source ↗PDF ↗Scholar ↗
2022

Physical Inertial Poser (PIP): Physics-Aware Real-Time Human Motion Tracking From Sparse Inertial Sensors

Xinyu Yi, Yuxiao Zhou, Marc Habermann, Soshi Shimada, Vladislav Golyanik, Christian Theobalt +1
CVPR 2022poster

Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera. However, capturing the motion and global posit…

Cited by 200PDF ↗Scholar ↗
2022

Playable Environments: Video Manipulation in Space and Time

Willi Menapace, Stéphane Lathuilière, Aliaksandr Siarohin, Christian Theobalt, Sergey Tulyakov, Vladislav Golyanik +1
CVPR 2022poster

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actio…

Cited by 20PDF ↗code ↗Scholar ↗
2022

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

Alp Yurtsever, Tolga Birdal, Vladislav Golyanik
ECCV 2022poster

"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). The computational premise of quantum computers has cultivated the re-design of various existing vision probl…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2022

Quantum Motion Segmentation

Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, Vladislav Golyanik
ECCV 2022poster

Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par perfo…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2022

UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture

Hiroyasu Akada, Jian Wang, Soshi Shimada, Masaki Takahashi, Christian Theobalt, Vladislav Golyanik
ECCV 2022poster

"We present UnrealEgo, a new large-scale naturalistic dataset for egocentric 3D human pose estimation. UnrealEgo is based on an advanced concept of eyeglasses equipped with two fisheye cameras that can be used in unconstrained environments. We design their virtual prototype and attach them to 3D hum…

Cited by 54Source ↗PDF ↗Scholar ↗
2022

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

Navami Kairanda, Edith Tretschk, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik
CVPR 2022poster

Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominan…

Cited by 24PDF ↗code ↗Scholar ↗
2021

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

Viktor Rudnev, Vladislav Golyanik, Jiayi Wang, Hans-Peter Seidel, Franziska Mueller, Mohamed Elgharib +1
ICCV 2021poster

3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor reacting on brightness changes. Our EventHands approach has c…

Cited by 62PDF ↗code ↗Scholar ↗
2021

Gravity-Aware Monocular 3D Human-Object Reconstruction

Rishabh Dabral, Soshi Shimada, Arjun Jain, Christian Theobalt, Vladislav Golyanik
ICCV 2021poster

This paper proposes GraviCap, i.e., a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos. We focus on scenes with objects partially observed during a free flight. In contrast to existing monocular methods, we can recover scale, object…

Cited by 31PDF ↗Scholar ↗
2021

High-Fidelity Neural Human Motion Transfer From Monocular Video

Moritz Kappel, Vladislav Golyanik, Mohamed Elgharib, Jann-Ole Henningson, Hans-Peter Seidel, Susana Castillo +2
CVPR 2021poster

Video-based human motion transfer creates video animations of humans following a source motion. Current methods show remarkable results for tightly-clad subjects. However, the lack of temporally consistent handling of plausible clothing dynamics, including fine and high-frequency details, significan…

Cited by 41PDF ↗Scholar ↗
2021

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

Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, Christian Theobalt
ICCV 2021poster

We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appea…

Cited by 557PDF ↗Scholar ↗
2021

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

Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik, Kripasindhu Sarkar, Hyun Soo Park, Christian Theobalt
CVPR 2021poster

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. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in pre…

Cited by 77PDF ↗Scholar ↗
2021

Q-Match: Iterative Shape Matching via Quantum Annealing

Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller
ICCV 2021poster

Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density. A promising research direction is to tackle such quadratic optimization problems over binary variables with quantum annealing, which allows…

Cited by 37PDF ↗Scholar ↗
2020

DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects

Edgar Tretschk, Ayush Tewari, Michael Zollhöfer, Vladislav Golyanik, Christian Theobalt
ECCV 2020poster

Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder \hbox{(DEMEA)} which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. The embedded deformation layer (EDL) is a different…

Cited by 51Source ↗PDF ↗Scholar ↗
2020

EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera

Lan Xu, Weipeng Xu, Vladislav Golyanik, Marc Habermann, Lu Fang, Christian Theobalt
CVPR 2020oral

The high frame rate is a critical requirement for capturing fast human motions. In this setting, existing markerless image-based methods are constrained by the lighting requirement, the high data bandwidth and the consequent high computation overhead. In this paper, we propose EventCap -- the first…

Cited by 124PDF ↗Scholar ↗
2020

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

Neng Qian, Jiayi Wang, Franziska Mueller, Florian Bernard, Vladislav Golyanik, Christian Theobalt
ECCV 2020poster

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. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve more r…

Cited by 87Source ↗PDF ↗Scholar ↗
2020

HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation From a Single Depth Map

Jameel Malik, Ibrahim Abdelaziz, Ahmed Elhayek, Soshi Shimada, Sk Aziz Ali, Vladislav Golyanik +2
CVPR 2020poster

3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due t…

Cited by 93PDF ↗Scholar ↗
2020

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

Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, Antonio Agudo, Christian Theobalt
ECCV 2020poster

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. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep…

Cited by 66Source ↗PDF ↗Scholar ↗
2020

Neural Re-Rendering of Humans from a Single Image

Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, Christian Theobalt
ECCV 2020poster

Human re-rendering from a single image is a starkly under-constrained problem and state-of-the-art algorithms often exhibit un-desired artefacts, such as oversmoothing, unrealistic distortions of thebody parts and garments, or implausible changes of the texture. To ad-dress these challenges, we prop…

Cited by 91Source ↗PDF ↗Scholar ↗
2020

PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations

Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Carsten Stoll, Christian Theobalt
ECCV 2020poster

Implicit surface representation combined with deep learning has led to impressive models which can represent detailed shapes of objects. Implicit surface representations, such as signed-distance functions, allow to represent shapes of arbitrary topologies. Since a continous function is learned, the…

Cited by 116Source ↗PDF ↗Scholar ↗
2019

Accelerated Gravitational Point Set Alignment With Altered Physical Laws

Vladislav Golyanik, Christian Theobalt, Didier Stricker
ICCV 2019poster

This work describes Barnes-Hut Rigid Gravitational Approach (BH-RGA) -- a new rigid point set registration method relying on principles of particle dynamics. Interpreting the inputs as two interacting particle swarms, we directly minimise the gravitational potential energy of the system using non-li…

Cited by 16PDF ↗code ↗Scholar ↗