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Found 12 papers, 6 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.
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control.
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
In contrast to methods that use alternate 3D shape representations (e. g. implicit representations), our approach is a discrete denoising diffusion probabilistic model that operates natively on the polygonal mesh data structure.
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields.
Text2Tex: Text-driven Texture Synthesis via Diffusion Models
We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts.
Panoptic Lifting for 3D Scene Understanding with Neural Fields
We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric representations from images of in-the-wild scenes.
DiffRF: Rendering-Guided 3D Radiance Field Diffusion
We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models.
Texturify: Generating Textures on 3D Shape Surfaces
Texture cues on 3D objects are key to compelling visual representations, with the possibility to create high visual fidelity with inherent spatial consistency across different views.
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks.
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion.
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation
To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy.
Clustering with Deep Learning: Taxonomy and New Methods
In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks.
