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Found 12 papers, 5 papers with code
Pippo: High-Resolution Multi-View Humans from a Single Image
Finally, we also introduce an improved metric to evaluate 3D consistency of multi-view generations, and show that Pippo outperforms existing works on multi-view human generation from a single image.
Fillerbuster: Multi-View Scene Completion for Casual Captures
Our solution is to train a generative model that can consume a large context of input frames while generating unknown target views and recovering image poses when desired.
Toon3D: Seeing Cartoons from New Perspectives
We recover the underlying 3D structure from images of cartoons and anime depicting the same scene.
NeRFiller: Completing Scenes via Generative 3D Inpainting
In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text.
Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory.
Nerfstudio: A Modular Framework for Neural Radiance Field Development
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
Studying Bias in GANs through the Lens of Race
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets.
The One Where They Reconstructed 3D Humans and Environments in TV Shows
TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications.
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
Scaling up instance annotation via label propagation
Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets.
Detecting natural disasters, damage, and incidents in the wild
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts.
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2D Semantic Segmentation
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