37 citations · 45 across the 10 of their papers we have counts for
11 papers
Bridging Frontier Reasoning and Robot Execution: From Autonomous Demonstration Generation to Dense Language Supervision
Bosung Kim, Alexander Trevithick, Ruiyi Wang +1
Recent advances in frontier models enable robot manipulation from only a few demonstrations, but high inference latency limits their use for real-time robot control. To bridge this…
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
Liyuan Zhu, Shengyu Huang, Amrita Mazumdar +6
We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, p…
How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning
Bosung Kim, Ruiyi Wang, David Acuna +5
Scaling robot policy learning is bottlenecked by the cost of collecting demonstrations, while language annotations for existing demonstrations are comparatively cheap. We study lan…
CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models
Rundi Wu, Ruiqi Gao, Ben Poole +4
We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets…
SimVS: Simulating World Inconsistencies for Robust View Synthesis
Alex Trevithick, Roni Paiss, Philipp Henzler +9
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illuminat…
RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion
Jaidev Shriram, Alex Trevithick, Lingjie Liu +1
We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex…
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
Alex Trevithick, Matthew Chan, Towaki Takikawa +5
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of…
PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN
Kai-En Lin, Alex Trevithick, Keli Cheng +5
Portrait synthesis creates realistic digital avatars which enable users to interact with others in a compelling way. Recent advances in StyleGAN and its extensions have shown promi…
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Alex Trevithick, Matthew Chan, Michael Stengel +7
We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our im…
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Jiatao Gu, Alex Trevithick, Kai-En Lin +4
Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the inpu…
GRF: Learning a General Radiance Field for 3D Representation and Rendering
Alex Trevithick, Bo Yang
We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a genera…