8 papers
GeoVolDiff: Taming 3D Geological Volumes with Latent Diffusion
Qi Pang, Hongling Chen, Jinghuai Gao
Deep learning has become a prevailing paradigm across a wide range of geophysical applications. Yet most existing studies concentrate on methodological refinements -- novel network…
Physical Object Understanding with a Physically Controllable World Model
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +9
A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that govern their interactions. Solvi…
Unified 3D Scene Understanding Through Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +4
Understanding 3D scenes requires flexible combinations of visual reasoning tasks, including depth estimation, novel view synthesis, and object manipulation, all of which are essent…
Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models
Pengcheng Fang, Hongli Chen, Xiaohao Cai
World action models jointly predict future video and action during training, raising an open question about what role the future-prediction branch actually plays. A recent finding…
Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation
Yutong Zhang, Jiaxin Chen, Honglin Chen +5
Memory-efficient transfer learning (METL) approaches have recently achieved promising performance in adapting pre-trained models to downstream tasks. They avoid applying gradient b…
World Modeling with Probabilistic Structure Integration
Klemen Kotar, Wanhee Lee, Rahul Venkatesh +13
We present Probabilistic Structure Integration (PSI), a system for learning richly controllable and flexibly promptable world models from data. PSI consists of a three-step cycle.…