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Found 11 papers, 8 papers with code
Meta 3D Gen
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation.
Lightplane: Highly-Scalable Components for Neural 3D Fields
Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision.
EucliDreamer: Fast and High-Quality Texturing for 3D Models with Depth-Conditioned Stable Diffusion
We present EucliDreamer, a simple and effective method to generate textures for 3D models given text prompts and meshes.
DreamGaussian4D: Generative 4D Gaussian Splatting
Specifically, we propose an integral framework with two major modules: 1) Image-to-4D GS - we initially generate static GS with DreamGaussianHD, followed by HexPlane-based dynamic generation with Gaussian deformation; and 2) Video-to-Video Texture Refinement - we refine the generated UV-space texture maps and meanwhile enhance their temporal consistency by utilizing a pre-trained image-to-video diffusion model.
EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth
This paper presents a novel method to generate textures for 3D models given text prompts and 3D meshes.
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input.
HexPlane: A Fast Representation for Dynamic Scenes
HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.
FWD: Real-time Novel View Synthesis with Forward Warping and Depth
Novel view synthesis (NVS) is a challenging task requiring systems to generate photorealistic images of scenes from new viewpoints, where both quality and speed are important for applications.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
Inverting and Understanding Object Detectors
As a core problem in computer vision, the performance of object detection has improved drastically in the past few years.
Unified Signal Compression Using Generative Adversarial Networks
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.
