1.2k citations · 1.3k across the 24 of their papers we have counts for
26 papers
Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction
Cheng-You Lu, Zhuoli Zhuang, Nguyen Thanh Trung Le +5
Advances in 3D reconstruction and novel view synthesis have enabled efficient and photorealistic rendering. However, images for reconstruction are still either largely manual or co…
ProcTex: Consistent and Interactive Text-to-texture Synthesis for Part-based Procedural Models
Ruiqi Xu, Zihan Zhu, Ben Ahlbrand +2
Recent advances in generative modeling have driven significant progress in text-guided texture synthesis. However, current methods focus on synthesizing texture for single static 3…
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
Rao Fu, Dingxi Zhang, Alex Jiang +4
Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale,…
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes
Xianghao Xu, Srinath Sridhar, Daniel Ritchie
We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optim…
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes
Rao Fu, Zehao Wen, Zichen Liu +1
Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text into well-structured and textured indoor scenes at a house-scale. By prompting Large Lang…
Strata-NeRF : Neural Radiance Fields for Stratified Scenes
Ankit Dhiman, Srinath R, Harsh Rangwani +4
Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settin…
DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields
Cheng-You Lu, Peisen Zhou, Angela Xing +8
Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes. However, their capabilities lag behind those offered by conventional…
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Bipasha Sen, Gaurav Singh, Aditya Agarwal +3
Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects. However, learning generalizable…
Semantic Attention Flow Fields for Monocular Dynamic Scene Decomposition
Yiqing Liang, Eliot Laidlaw, Alexander Meyerowitz +2
From video, we reconstruct a neural volume that captures time-varying color, density, scene flow, semantics, and attention information. The semantics and attention let us identify…
SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping
Bipasha Sen, Aditya Agarwal, Gaurav Singh +3
Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods tackle th…
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…
Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields
Rohith Agaram, Shaurya Dewan, Rahul Sajnani +3
Coordinate-based implicit neural networks, or neural fields, have emerged as useful representations of shape and appearance in 3D computer vision. Despite advances, however, it rem…
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen +2
We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt…
Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
Xianghao Xu, Yifan Ruan, Srinath Sridhar +1
3D models of manufactured objects are important for populating virtual worlds and for synthetic data generation for vision and robotics. To be most useful, such objects should be a…
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
Trevor Houchens, Cheng-You Lu, Shivam Duggal +2
In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for…
Learning Body-Aware 3D Shape Generative Models
Bryce Blinn, Alexander Ding, R. Kenny Jones +3
The shape of many objects in the built environment is dictated by their relationships to the human body: how will a person interact with this object? Existing data-driven generativ…
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes
Rahul Sajnani, Adrien Poulenard, Jivitesh Jain +3
Progress in 3D object understanding has relied on manually canonicalized shape datasets that contain instances with consistent position and orientation (3D pose). This has made it…
StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects
Ge Zhang, Or Litany, Srinath Sridhar +1
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images. Reconstructing general articulating object categori…
HuMoR: 3D Human Motion Model for Robust Pose Estimation
Davis Rempe, Tolga Birdal, Aaron Hertzmann +3
We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from…
Predicting the Physical Dynamics of Unseen 3D Objects
Davis Rempe, Srinath Sridhar, He Wang +1
Machines that can predict the effect of physical interactions on the dynamics of previously unseen object instances are important for creating better robots and interactive virtual…
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang +3
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem as…
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller +4
We propose a new single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our approach uses novel occlusion-robust pose-maps (ORPM) whi…
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko +6
We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combine…