Search Results for author:
Found 11 papers, 4 papers with code
ZeroRF: Fast Sparse View 360° Reconstruction with Zero Pretraining
We present ZeroRF, a novel per-scene optimization method addressing the challenge of sparse view 360{\deg} reconstruction in neural field representations.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.
Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view.
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
We present a method for generating high-quality watertight manifold meshes from multi-view input images.
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI.
Factor Fields: A Unified Framework for Neural Fields and Beyond
Our experiments show that DiF leads to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods.
Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search
Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape.
Nuisance-Label Supervision: Robustness Improvement by Free Labels
Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them.
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution
We show in this work that Monte Carlo path tracing can be further accelerated by joint super-resolution and denoising (SRD) in post-processing.
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
This can explain why an activity classification model usually fails to generalize to datasets it is not trained on.
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition
In this paper, we propose the Randomized Simulation as Augmentation (RSA) framework which augments real-world training data with synthetic data to improve the robustness of action recognition networks.
