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cs.CV2026
DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution
Hidir Yesiltepe, Koutilya PNVR, Gaurav Pathak +4
Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the strong coupling between con…
cs.CV2024
InVi: Object Insertion In Videos Using Off-the-Shelf Diffusion Models
Nirat Saini, Navaneeth Bodla, Ashish Shrivastava +4
We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets…
cs.CV2024
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR
Bharat Singh, Viveka Kulharia, Luyu Yang +3
Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail. We propose a novel approach, GenMM, for join…