7 papers · 1 filter
GEOPHYS: The Geometry of Physical Plausibility
Christian Internò, Alexander Pondaven, Habon Issa +8
While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow and expensive. They either r…
ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
Omar El Khalifi, Thomas Rossi, Oscar Fossey +6
For artistic applications, video generation requires fine-grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present A…
Learning to Generate Rigid Body Interactions with Video Diffusion Models
David Romero, Ariana Bermudez, Viacheslav Iablochnikov +3
Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such model…
MessyKitchens: Contact-rich object-level 3D scene reconstruction
Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati +1
Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance i…
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
Jianhao Yuan, Fabio Pizzati, Francesco Pinto +5
Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such…
InterPose: Learning to Generate Human-Object Interactions from Large-Scale Web Videos
Yangsong Zhang, Abdul Ahad Butt, Gül Varol +1
Human motion generation has shown great advances thanks to the recent diffusion models trained on large-scale motion capture data. Most of existing works, however, currently target…