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Found 14 papers, 2 papers with code
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects.
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.
Meta 3D Gen
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation.
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
Mosaic-SDF for 3D Generative Models
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes.
SpaText: Spatio-Textual Representation for Controllable Image Generation
Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based.
Make-A-Video: Text-to-Video Generation without Text-Video Data
We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V).
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Recent text-to-image models have achieved impressive results.
Ranked #32 on
Text-to-Image Generation
on MS COCO
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains.
Ranked #19 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Single-Shot Freestyle Dance Reenactment
The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer.
Low Bandwidth Video-Chat Compression using Deep Generative Models
To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network.
Wish You Were Here: Context-Aware Human Generation
We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene.
Live Face De-Identification in Video
We propose a method for face de-identification that enables fully automatic video modification at high frame rates.
Vid2Game: Controllable Characters Extracted from Real-World Videos
The second network maps the current pose, the new pose, and a given background, to an output frame.
