Sanja Fidler
Associate Professor
Studies 3D computer vision and generative models for content creation and simulation.
Sanja Fidler's lab at Toronto works on 3D vision & neural rendering, generative & foundation models and physics-based simulation & animation. 63 papers since 2023, 61 at top venues, mostly CVPR and ICCV. 13 PhD students and 8 master's students. 61 alumni, most often to Google and Amazon. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 97%
- PhD students
- 13
- MS students
- 8
- alumni listed
- 61
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 6 · collecting
- 16 papers2025 · T1 15
- 21 papers2024 · T1 21
- 20 papers2023 · T1 19
T1 venuesOtherVenues: CVPR, ICCV, ICLR, NeurIPS, ECCV
Research focuspaper titles that use each topic's words
- Generative & foundation models18 papers
- 3D vision & neural rendering12 papers
- Physics-based simulation & animation6 papers
- content creation1 paper
- creation1 paper
Paper list
From DBLP.
20266 papers
- ICLR (T1)ChronoEdit: Towards Temporal Reasoning for In-Context Image Editing and World Simulation (opens in a new tab)
- CVPR (T1)DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer (opens in a new tab)
- ICLR (T1)Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation (opens in a new tab)
- ECCV (T1)MoRight: Motion Control Done Right (opens in a new tab)
- ECCV (T1)RadarGen: Automotive Radar Point Cloud Generation from Cameras (opens in a new tab)
- ICLR (T1)SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer (opens in a new tab)
202516 papers
- NeurIPS (T1)Align Your Flow: Scaling Continuous-Time Flow Map Distillation (opens in a new tab)
- CVPR (T1)Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves? (opens in a new tab)
- ICCV (T1)Controllable Weather Synthesis and Removal with Video Diffusion Models (opens in a new tab)
- CVPR (T1)Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models (opens in a new tab)
- CVPR (T1)DIFIX3D+: Improving 3D Reconstructions with Single-Step Diffusion Models (opens in a new tab)
- NeurIPS (T1)Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos (opens in a new tab)
- CVPR (T1)GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control (opens in a new tab)
- ICCV (T1)InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models (opens in a new tab)
- NeurIPS (T1)LuxDiT: Lighting Estimation with Video Diffusion Transformer (opens in a new tab)
- ICLR (T1)OmniRe: Omni Urban Scene Reconstruction (opens in a new tab)
- JMLR (T1)Optimizing Data Collection for Machine Learning (opens in a new tab)
- ICCV (T1)PartField: Learning 3D Feature Fields for Part Segmentation and Beyond (opens in a new tab)
202421 papers
- TOG (T1)3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes (opens in a new tab)
- CVPR (T1)3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features (opens in a new tab)
- CVPR (T1)Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models (opens in a new tab)
- ICML (T1)Align Your Steps: Optimizing Sampling Schedules in Diffusion Models (opens in a new tab)
- NeurIPS (T1)DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features (opens in a new tab)
- ICLR (T1)EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models (opens in a new tab)
- ICLR (T1)EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision (opens in a new tab)
- TOG (T1)fVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial Intelligence (opens in a new tab)
- NeurIPS (T1)L4GM: Large 4D Gaussian Reconstruction Model (opens in a new tab)
- ECCV (T1)LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis (opens in a new tab)
- ECCV (T1)NeRF-XL: Scaling NeRFs with Multiple GPUs (opens in a new tab)
- CVPR (T1)Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata (opens in a new tab)
202320 papers
- TOG (T1)Adaptive Shells for Efficient Neural Radiance Field Rendering (opens in a new tab)
- CVPR (T1)Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (opens in a new tab)
- ICCV (T1)ATT3D: Amortized Text-to-3D Object Synthesis (opens in a new tab)
- Trans. Mach. Learn. Res.Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting (opens in a new tab)
- SIGGRAPH Asia (T1)Compact Neural Graphics Primitives with Learned Hash Probing (opens in a new tab)
- ICCV (T1)DreamTeacher: Pretraining Image Backbones with Deep Generative Models (opens in a new tab)
- ICCV (T1)End-to-end 3D Tracking with Decoupled Queries (opens in a new tab)
- TOG (T1)Flexible Isosurface Extraction for Gradient-Based Mesh Optimization (opens in a new tab)
- ICCV (T1)Learning Human Dynamics in Autonomous Driving Scenarios (opens in a new tab)
- TOG (T1)Learning Physically Simulated Tennis Skills from Broadcast Videos (opens in a new tab)
- CVPR (T1)Magic3D: High-Resolution Text-to-3D Content Creation (opens in a new tab)
- CVPR (T1)Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes (opens in a new tab)
Where alumni went61 listed, 37% to industry
Industry 13Academia 22
Most often to
- Amazon
- Apple
Also to
- Stanford University
- UC Berkeley
- Carnegie Mellon University
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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