722 citations · 5.1k across the 126 of their papers we have counts for
106 papers · 1 filter
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
Daiqing Li, Huan Ling, Amlan Kar +5
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. W…
Neural Kernel Surface Reconstruction
Jiahui Huang, Zan Gojcic, Matan Atzmon +3
We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel…
Neural LiDAR Fields for Novel View Synthesis
Shengyu Huang, Zan Gojcic, Zian Wang +5
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from…
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
Seung Wook Kim, Bradley Brown, Kangxue Yin +6
Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce…
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes
Zian Wang, Tianchang Shen, Jun Gao +6
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods…
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
Davis Rempe, Zhengyi Luo, Xue Bin Peng +5
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in gui…
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Andreas Blattmann, Robin Rombach, Huan Ling +4
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent s…
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
Yiming Li, Zhiding Yu, Christopher Choy +5
Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in A…
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu, David Acuna, Andrew Liao +5
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domai…
Magic3D: High-Resolution Text-to-3D Content Creation
Chen-Hsuan Lin, Jun Gao, Luming Tang +7
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesi…
LION: Latent Point Diffusion Models for 3D Shape Generation
Xiaohui Zeng, Arash Vahdat, Francis Williams +4
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generat…
XDGAN: Multi-Modal 3D Shape Generation in 2D Space
Hassan Abu Alhaija, Alara Dirik, André Knörig +2
Generative models for 2D images has recently seen tremendous progress in quality, resolution and speed as a result of the efficiency of 2D convolutional architectures. However it i…
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
Ahmad Darkhalil, Dandan Shan, Bin Zhu +6
We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS,…
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Jun Gao, Tianchang Shen, Zian Wang +6
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of…
Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion
Zian Wang, Wenzheng Chen, David Acuna +2
We consider the challenging problem of outdoor lighting estimation for the goal of photorealistic virtual object insertion into photographs. Existing works on outdoor lighting esti…
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
Gopal Sharma, Kangxue Yin, Subhransu Maji +3
We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface represen…
Improving Semantic Segmentation in Transformers using Hierarchical Inter-Level Attention
Gary Leung, Jun Gao, Xiaohui Zeng +1
Existing transformer-based image backbones typically propagate feature information in one direction from lower to higher-levels. This may not be ideal since the localization abilit…
Variable Bitrate Neural Fields
Towaki Takikawa, Alex Evans, Jonathan Tremblay +4
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art r…
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps
Seung Wook Kim, Karsten Kreis, Daiqing Li +2
Modern image generative models show remarkable sample quality when trained on a single domain or class of objects. In this work, we introduce a generative adversarial network that…
MBEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
Enze Xie, Zhiding Yu, Daquan Zhou +5
In this paper, we propose MBEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inp…
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis
Zhiqin Chen, Kangxue Yin, Sanja Fidler
In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either a…
Learning Smooth Neural Functions via Lipschitz Regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson +2
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields are commonly represented as neural networks which map latent descriptors and 3D…
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Cinjon Resnick, Or Litany, Amlan Kar +4
Modern computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low…
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
Daiqing Li, Huan Ling, Seung Wook Kim +4
Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via…