147 citations · 355 across the 24 of their papers we have counts for
7 papers · 1 filter
Fast-SNARF: A Fast Deformer for Articulated Neural Fields
Xu Chen, Tianjian Jiang, Jie Song +4
Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects,…
PlanT: Explainable Planning Transformers via Object-Level Representations
Katrin Renz, Kashyap Chitta, Otniel-Bogdan Mercea +3
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene. While human drivers prioritize important objects and ignore details not…
ARAH: Animatable Volume Rendering of Articulated Human SDFs
Shaofei Wang, Katja Schwarz, Andreas Geiger +1
Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-ar…
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger +3
How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g. object detection, motion for…
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Niklas Hanselmann, Katrin Renz, Kashyap Chitta +2
Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit naïve behavior models for background traffic. Ha…
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Zijian Dong, Chen Guo, Jie Song +3
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…