We propose NeRFiller an approach that completes missing portions of a 3D capture via
generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a …

Diffusion models currently achieve state-of-the-art performance for both conditional and
unconditional image generation. However, so far, image diffusion models do not support …

Recent advances in generative imagery have brought forth outpainting and inpainting
models that can produce high-quality, plausible image content in unknown regions …

Image completion is a technique that allows lling-in target regions with alternative contents.
This allows removing unwanted objects or generating occluded regions for image-based 3D …

Video inpainting aims at filling in missing regions of a video. However, when dealing with
dynamic scenes with camera or object movements, annotating the inpainting target …

Recent deep generative inpainting methods use attention layers to allow the generator to
explicitly borrow feature patches from the known region to complete a missing region. Due to …

We address the challenge of recovering an underlying scene geometry and colors from a
sparse set of RGBD view observations. In this work, we present a new solution termed …

Recent advances in convolutional neural networks have shown promising results in 3D
shape completion. But due to GPU memory limitations, these methods can only produce low …

Existing image inpainting methods typically fill holes by borrowing information from
surrounding pixels. They often produce unsatisfactory results when the holes overlap with or …

Scene-agnostic visual inpainting remains very challenging despite progress in patch-based
methods. Recently, Pathak et al.[26] have introduced convolutional" context encoders''(CEs) …