6 citations · 7 across the 8 of their papers we have counts for
8 papers
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker +4
Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graph…
iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis
Yash Kant, Aliaksandr Siarohin, Michael Vasilkovsky +4
We present a method for generating consistent novel views from a single source image. Our approach focuses on maximizing the reuse of visible pixels from the source image. To achie…
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
Boris Ivanovic, Guanyu Song, Igor Gilitschenski +1
The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autono…
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Xiao Li, Igor Gilitschenski, Guy Rosman +2
As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provi…
Reference-guided Controllable Inpainting of Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and contro…
CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videos
Tianshu Kuai, Akash Karthikeyan, Yash Kant +2
Animating an object in 3D often requires an articulated structure, e.g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movem…