11 citations · 12 across the 9 of their papers we have counts for
9 papers
GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill
Chenyue Cai, Anita Hu, James Lucas +2
Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-of-the-art generative methods st…
Addressable Memory for Video World Models
Xindi Wu, Sven Elflein, James Lucas +5
We study visual persistence in interactive video world models. These models rely on a Key-Value (KV) cache as a growing visual memory to carry forward previously generated frames.…
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
Variance Reduction for Expectations with Diffusion Teachers
Jesse Bettencourt, Xindi Wu, Matan Atzmon +2
Pretrained diffusion models serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution. The teacher gradients these pi…
Multi-student Diffusion Distillation for Better One-step Generators
Yanke Song, Jonathan Lorraine, Weili Nie +2
Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student…
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
Tianchang Shen, Zhaoshuo Li, Marc Law +5
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or…
Improving Hyperparameter Optimization with Checkpointed Model Weights
Nikhil Mehta, Jonathan Lorraine, Steve Masson +4
When training deep learning models, the performance depends largely on the selected hyperparameters. However, hyperparameter optimization (HPO) is often one of the most expensive p…
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
Kevin Xie, Jonathan Lorraine, Tianshi Cao +5
Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D o…
Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law +2
Neural networks efficiently encode learned information within their parameters. Consequently, many tasks can be unified by treating neural networks themselves as input data. When d…