4 papers
Learning Massively Multitask World Models for Continuous Control
Nicklas Hansen, Hao Su, Xiaolong Wang
General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-ta…
RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets
Isabella Liu, Zhan Xu, Wang Yifan +5
We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints and skeleton topologies and assigning…
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S, Vlad Sobal +3
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…
Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes
Isabella Liu, Hao Su, Xiaolong Wang
Modern 3D engines and graphics pipelines require mesh as a memory-efficient representation, which allows efficient rendering, geometry processing, texture editing, and many other d…