This is the official GitHub account for the Computer Vision and Machine Learning Group at the University of Illinois Urbana-Champaign. We study a wide range of problems in visual understanding, data-efficient learning, and intelligent perception. Our research addresses fundamental challenges in computer vision, machine learning, and multimodal learning, while also exploring real-world applications across domains such as healthcare and interactive systems.
CVML Group @ Illinois
- 11 followers
- United States of America
- https://vision.ischool.illinois.edu/
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Repositories
- FO4DSMPLX Public Forked from howardziyuantang/FO4DSMPLX
Human-specific SMPL-X extension of FreeOrbit4D: a modular, training-free 4D human-scene reconstruction and camera redirection framework.
- Perturbed-Gaussian-Ensemble Public Forked from yulunwu0108/Perturbed-Gaussian-Ensemble
[ECCV 2026] Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction
- .github Public
- FreeOrbit4D Public Forked from VVeiCao/FreeOrbit4D
[SIGGRAPH 2026 Conference] FreeOrbit4D: Training-free Arbitrary Camera Redirection for Monocular Videos via Foreground-Complete 4D Reconstruction
- redirect4d-bench-code Public Forked from VVeiCao/redirect4d-bench
Redirect4D-Bench: A Scalable Benchmark for Camera Redirection of Monocular Dynamic Videos with Pseudo-4D Ground Truth
- MetaPEFT Public Forked from doem97/metalora
[CVPR 2025 Highlight] Meta-Learning Hyperparameters for Parameter-Efficient Fine-Tuning
- 3D-Diffusion-Style-Transfer Public Forked from wufeim/DST3D
[ICLR 2024 Spotlight] Generating images with 3D annotations using diffusion models
- HDR-GS Public Forked from caiyuanhao1998/HDR-GS
"HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting" (NeurIPS 2024)
- Large-Multimodal-Models-Continual-Learning Public Forked from jessemelpolio/LMM_CL
[ECCV 2026 Oral] How to Teach Large Multimodal Models New Skills?
- PESCR Public Forked from KerryDRX/PESCR
Official implementation of Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning [BMVC 2025].
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