activity
20122026
most citedDetect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts

92 citations · 437 across the 40 of their papers we have counts for

collaborators

40 papers

cs.GR2026

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

NVIDIA, :, Jiahui Huang +14

3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent yea…

cs.LG20245 cited

SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation

Jordan Juravsky, Yunrong Guo, Sanja Fidler +1

Physically-simulated models for human motion can generate high-quality responsive character animations, often in real-time. Natural language serves as a flexible interface for cont…

cs.CV20241 cited

L4GM: Large 4D Gaussian Reconstruction Model

Jiawei Ren, Kevin Xie, Ashkan Mirzaei +8

We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Ke…

cs.CV2024

Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata

Dongsu Zhang, Francis Williams, Zan Gojcic +4

We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion, we…

cs.CV2024

NeRF-XL: Scaling NeRFs with Multiple GPUs

Ruilong Li, Sanja Fidler, Angjoo Kanazawa +1

We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily…

cs.CV20241 cited

Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis

Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sam…