patriciagschossmann.github.io
@aclmeeting.bsky.social with personalized conference programs this year for the first time!
www.scholar-inbox.com/conference/a...
It would be great if you could widely share this news within the NLP community. See you next week in Vienna!
youtube.com/watch?v=_god...
youtube.com/watch?v=_god...
The repository contains the first public code base for training RL agents with the CARLA leaderboard 2.0 and nuPlan.
github.com/autonomousvi...
The repository contains the first public code base for training RL agents with the CARLA leaderboard 2.0 and nuPlan.
github.com/autonomousvi...
Philipp Hühn and @markusflicke.bsky.social impressed everyone with a clear and engaging pitch of their smart recommender tool for academic papers.
Philipp Hühn and @markusflicke.bsky.social impressed everyone with a clear and engaging pitch of their smart recommender tool for academic papers.
raniatze.github.io/pritti/
raniatze.github.io/pritti/
www.youtube.com/watch?v=HfHC...
www.youtube.com/watch?v=HfHC...
A Vision-Language-Action (VLA) model that achieves state-of-the-art driving performance with language capabilities.
Code: github.com/RenzKa/simli...
Paper: arxiv.org/abs/2503.09594
A Vision-Language-Action (VLA) model that achieves state-of-the-art driving performance with language capabilities.
Code: github.com/RenzKa/simli...
Paper: arxiv.org/abs/2503.09594
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
We show how simple rewards enable scaling up PPO for planning.
CaRL outperforms all prior learning-based approaches on nuPlan Val14 and CARLA longest6 v2, using less inference compute.
arxiv.org/abs/2504.17838
We show how simple rewards enable scaling up PPO for planning.
CaRL outperforms all prior learning-based approaches on nuPlan Val14 and CARLA longest6 v2, using less inference compute.
arxiv.org/abs/2504.17838
(1) generate motion from sequence- and frame-level text,
(2) generate detailed per-frame motion descriptions, and
(3) generate motion from random noise.
coral79.github.io/uni-motion/
(1) generate motion from sequence- and frame-level text,
(2) generate detailed per-frame motion descriptions, and
(3) generate motion from random noise.
coral79.github.io/uni-motion/
andrehuang.github.io/renovate/
andrehuang.github.io/renovate/
@bernhard-jaeger.bsky.social
www.nowpublishers.com/article/Deta...
arxiv.org/abs/2312.08365
@bernhard-jaeger.bsky.social
www.nowpublishers.com/article/Deta...
arxiv.org/abs/2312.08365