ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1905.11169
  4. Cited By
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation
  from Video

Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video

27 May 2019
Miguel Jaques
Michael G. Burke
Timothy M. Hospedales
    VGen
    PINN
ArXivPDFHTML

Papers citing "Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video"

11 / 11 papers shown
Title
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Alejandro Castañeda Garcia
J. C. V. Gemert
Daan Brinks
Nergis Tömen
36
0
0
02 Oct 2024
Improving Physics-Augmented Continuum Neural Radiance Field-Based
  Geometry-Agnostic System Identification with Lagrangian Particle Optimization
Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimization
Takuhiro Kaneko
35
2
0
06 Jun 2024
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
Carlos Rodriguez-Pardo
Melania Prieto-Martin
Dan Casas
Elena Garces
23
12
0
13 Apr 2023
ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered
  Clothes
ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
Dohae Lee
Hyun Kang
In-Kwon Lee
3DH
AI4CE
32
7
0
07 Apr 2023
Neural Implicit Representations for Physical Parameter Inference from a
  Single Video
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Florian Hofherr
Lukas Koestler
Florian Bernard
Daniel Cremers
AI4CE
32
9
0
29 Apr 2022
Dissipative Hamiltonian Neural Networks: Learning Dissipative and
  Conservative Dynamics Separately
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately
A. Sosanya
S. Greydanus
PINN
AI4CE
27
26
0
25 Jan 2022
Discovering Latent Representations of Relations for Interacting Systems
Discovering Latent Representations of Relations for Interacting Systems
Dohae Lee
Young-Jin Oh
In-Kwon Lee
BDL
19
1
0
10 Nov 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Rui Wang
Rose Yu
AI4CE
PINN
37
64
0
02 Jul 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable
  Generative Modeling
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Naoya Takeishi
Alexandros Kalousis
DRL
AI4CE
22
54
0
25 Feb 2021
A Compositional Object-Based Approach to Learning Physical Dynamics
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang
T. Ullman
Antonio Torralba
J. Tenenbaum
AI4CE
OCL
238
438
0
01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
278
1,400
0
01 Dec 2016
1