2.9k citations · 10.7k across the 136 of their papers we have counts for
123 papers · 1 filter
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
Yijia Weng, Kaichun Mo, Ruoxi Shi +2
Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure…
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
Yang Zheng, Adam W. Harley, Bokui Shen +2
We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal i…
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis
Nilesh Kulkarni, Davis Rempe, Kyle Genova +4
We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific…
Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions
Ian Huang, Vrishab Krishna, Omoruyi Atekha +1
What constitutes the "vibe" of a particular scene? What should one find in "a busy, dirty city street", "an idyllic countryside", or "a crime scene in an abandoned living room"? Th…
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
Congyue Deng, Jiahui Lei, Bokui Shen +2
Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulate…
NAP: Neural 3D Articulation Prior
Jiahui Lei, Congyue Deng, Bokui Shen +2
We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models. Despite the extensive research on generating 3D object…
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
Connor Z. Lin, Koki Nagano, Jan Kautz +5
There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for ed…
DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion
Kiyohiro Nakayama, Mikaela Angelina Uy, Jiahui Huang +3
While the community of 3D point cloud generation has witnessed a big growth in recent years, there still lacks an effective way to enable intuitive user control in the generation p…
Learning a Diffusion Prior for NeRFs
Guandao Yang, Abhijit Kundu, Leonidas J. Guibas +2
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many…
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Bokui Shen, Xinchen Yan, Charles R. Qi +5
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.…
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
Xiaomeng Xu, Yanchao Yang, Kaichun Mo +3
We propose a method that trains a neural radiance field (NeRF) to encode not only the appearance of the scene but also semantic correlations between scene points, regions, or entit…
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
Bingfan Zhu, Yanchao Yang, Xulong Wang +2
We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variat…
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision
Jiahui Lei, Congyue Deng, Karl Schmeckpeper +2
We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple, effective, and robust geometric algorithm that can segment objects in 3D scenes without annotations…
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates
Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas +1
Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance…
PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan +5
Impressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However, being able to locally control and e…
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
Yichen Li, Kaichun Mo, Yueqi Duan +5
Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focu…
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision
Xiaoshuai Zhang, Abhijit Kundu, Thomas Funkhouser +3
We address efficient and structure-aware 3D scene representation from images. Nerflets are our key contribution -- a set of local neural radiance fields that together represent a s…
CC3D: Layout-Conditioned Generation of Compositional 3D Scenes
Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou +4
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images. Diffe…
CurveCloudNet: Processing Point Clouds with 1D Structure
Colton Stearns, Davis Rempe, Jiateng Liu +5
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1D curve-like structures. In this work, we introduce a…
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
Qiuhong Anna Wei, Sijie Ding, Jeong Joon Park +4
Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as s…
LADIS: Language Disentanglement for 3D Shape Editing
Ian Huang, Panos Achlioptas, Tianyi Zhang +3
Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing dec…
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
Zhen Wang, Shijie Zhou, Jeong Joon Park +5
This work introduces alternating latent topologies (ALTO) for high-fidelity reconstruction of implicit 3D surfaces from noisy point clouds. Previous work identifies that the spatia…
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
Congyue Deng, Chiyu "Max'' Jiang, Charles R. Qi +4
2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this obser…
MetaCLUE: Towards Comprehensive Visual Metaphors Research
Arjun R. Akula, Brendan Driscoll, Pradyumna Narayana +9
Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creativ…