Image Outpainting
22 papers with code • 3 benchmarks • 4 datasets
Predicting the visual context of an image beyond its boundary.
Image credit: NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Latest papers with no code
Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting
To this end, we propose a transformer-based 360 image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360 images.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision.
NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation
In various applications, such as robotic navigation and remote visual assistance, expanding the field of view (FOV) of the camera proves beneficial for enhancing environmental perception.
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames.
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation
We generate instructions for paths in our PanoGen environments with a speaker built on a pre-trained vision-and-language model for VLN pre-training, and augment the visual observation with our panoramic environments during agents' fine-tuning to avoid overfitting to seen environments.
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
3D photography renders a static image into a video with appealing 3D visual effects.
Structure-guided Image Outpainting
we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation.
Memory Efficient Patch-based Training for INR-based GANs
However, training existing approaches require a heavy computational cost proportional to the image resolution, since they compute an MLP operation for every (x, y) coordinate.
Scene Graph Expansion for Semantics-Guided Image Outpainting
In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information.
Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance
Concretely, we propose a Depth-Guided Outpainting Network to model different feature representations of two modalities and learn the structure-aware cross-modal fusion.