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Found 11 papers, 1 papers with code
PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation
Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness.
WaveMo: Learning Wavefront Modulations to See Through Scattering
Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy.
TimeRewind: Rewinding Time with Image-and-Events Video Diffusion
Through extensive experimentation, we demonstrate the capability of our approach to synthesize high-quality videos that effectively ``rewind'' time, showcasing the potential of combining event camera technology with generative models.
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations
tmospheric turbulence presents a significant challenge in long-range imaging.
Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation
In this paper, we introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models.
Continuous Levels of Detail for Light Field Networks
Furthermore, we use saliency-based importance sampling which enables our light field networks to distribute their capacity, particularly limited at lower LODs, towards representing the details viewers are most likely to focus on.
3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields
Motion magnification helps us visualize subtle, imperceptible motion.
Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images
The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality.
3D Motion Magnification: Visualizing Subtle Motions from Time-Varying Radiance Fields
Motion magnification helps us visualize subtle, imperceptible motion.
StegaNeRF: Embedding Invisible Information within Neural Radiance Fields
Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights.
