Tobias Pfaff
  • About
  • Publications
  • Projects
  • Publications
    • NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
    • A review of learning-based dynamics models for robotic manipulation
    • Cosmos-Drive-Dreams: Scalable synthetic driving data generation with world foundation models
    • Motion prompting: Controlling video generation with motion trajectories
    • A Review of Graph Neural Network Applications in Mechanics-related Domains
    • Imagen 3
    • Learning rigid-body simulators over implicit shapes
    • Learning rigid dynamics with face interaction graph networks
    • Graph network simulators can learn discontinuous, rigid contact dynamics
    • Learning 3D Particle-based Simulators from RGB-D Videos
    • Multiscale meshgraphnets
    • Physical Design using Differentiable Learned Simulators
    • Predicting Physics in Mesh-reduced Space with Temporal Attention
    • Constraint-based graph network simulator
    • Learned Coarse Models for Efficient Turbulence Simulation
    • Learning ground states of quantum hamiltonians with graph networks
    • Learning mesh-based simulation with graph networks
    • Combining q-learning and search with amortized value estimates
    • Learning to simulate complex physics with graph networks
    • Grandmaster level in StarCraft II using multi-agent reinforcement learning
    • One-shot high-fidelity imitation: Training large-scale deep nets with rl
    • Playing hard exploration games by watching youtube
    • Adaptive tearing and cracking of thin sheets
    • Folding and crumpling adaptive sheets
    • Lagrangian vortex sheets for animating fluids
    • Scalable fluid simulation using anisotropic turbulence particles
    • Field-scale apparent hydraulic parameterisation obtained from TDR time series and inverse modelling
    • Synthetic turbulence using artificial boundary layers
  • Projects
    • OmniDreams
    • Imagen 3
    • Veo
    • Grim Fandango Remastered
    • ARCSim
    • MantaFlow

Motion prompting: Controlling video generation with motion trajectories

Mar 1, 2025·
D Geng
,
C Herrmann
,
J Hur
,
F Cole
,
S Zhang
,
T Pfaff
,
T Lopez
,
Y Aytar
,
S Deqing
· 0 min read
PDF Cite Project
Type
Article-Conference
Publication
CVPR 2025
Last updated on Mar 1, 2025
← Cosmos-Drive-Dreams: Scalable synthetic driving data generation with world foundation models Apr 1, 2025
A Review of Graph Neural Network Applications in Mechanics-related Domains Oct 1, 2024 →

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