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Found 6 papers, 0 papers with code
Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation
In this work, we introduce Prometheus, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds.
Learning Temporally Consistent Video Depth from Video Diffusion Priors
Moreover, we design an effective training strategy to provide context within a clip.
MaPa: Text-driven Photorealistic Material Painting for 3D Shapes
Instead of relying on extensive paired data, i. e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs.
Learning 3D-Aware GANs from Unposed Images with Template Feature Field
Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice.
UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields
Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation.
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy
Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure.
