Sanja Fidler

Latest

• DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
• Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models
• GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
• ReMatching Dynamic Reconstruction Flow
• SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
• 3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes
• SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
• LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
• Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
• fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
• SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
• Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
• Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata
• Adaptive Shells for Efficient Neural Radiance Field Rendering
• Compact Neural Graphics Primitives with Learned Hash Probing
• TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
• ATT3D: Amortized Text-to-3D Object synthesis
• DreamTeacher: Pretraining Image Backbones with Deep Generative Models
• Learning Human Dynamics in Autonomous Driving Scenarios
• Neural LiDAR Fields for Novel View Synthesis
• Towards Viewpoint Robustness in Bird's Eye View Segmentation
• Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
• Learning Physically Simulated Tennis Skills from Broadcast Videos
• Synthesizing Physical Character-Scene Interactions
• Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
• Magic3D: High-Resolution Text-to-3D Content Creation
• Neural Fields meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes
• Neural Kernel Surface Reconstruction
• NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
• Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
• VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
• Neural Brushstroke Engine: Learning a Latent Style Space of Interactive Drawing Tools
• GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
• LION: Latent Point Diffusion Models for 3D Shape Generation
• Optimizing data collection for machine learning
• PADL: Language-Directed Physics-Based Character Control
• XDGAN: Multi-Modal 3D Shape Generation in 2D Space
• Kaolin Wisp: A PyTorch Library and Engine for Neural Fields Research
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
• Learning Smooth Neural Functions via Lipschitz Regularization
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
• Variable Bitrate Neural Fields
• Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps
• Extracting Triangular 3D Models, Materials, and Lighting From Images
• AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis
• BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
• Frame Averaging for Equivariant Shape Space Learning
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
• How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
• Neural Fields as Learnable Kernels for 3D Reconstruction
• Domain Adversarial Training: A Game Perspective
• Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
• ATISS: Autoregressive Transformers for Indoor Scene Synthesis
• Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
• EditGAN: High-Precision Semantic Image Editing
• Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting
• 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations
• Physics-based Human Motion Estimation and Synthesis from Videos
• f-Domain-Adversarial Learning: Theory and Algorithms
• Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
• DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
• DriveGAN: Towards a Controllable High-Quality Neural Simulation
• Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
• Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
• gradSim: Differentiable simulation for system identification and visuomotor control
• UniCon: Universal Neural Controller For Physics-based Character Motion
• Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
• Variational Amodal Object Completion
• Learning Deformable Tetrahedral Meshes for 3D Reconstruction
• Federated Simulation or Medical Imaging
• Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation
• Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
• Learning to Evaluate Perception Models Using Planner-Centric Metrics
• Learning to Simulate Dynamic Environments with GameGAN
• Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
• Neural Turtle Graphics for Modeling City Road Layouts
• Meta Sim: Learning to Generate Synthetic Datasets
• Gated-SCNN Gated Shape CNNs for Semantic Segmentation
• Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations