Text-to-3D

3DTopia-XL can generate high-quality 3D PBR assets from text or image inputs in just 5 seconds.

Disentangled Clothed Avatar Generation from Text Descriptions can create high-quality 3D avatars by separately modeling human bodies and clothing. This method improves texture and geometry quality and aligns well with text prompts, enhancing virtual try-on and character animation.

DreamHOI can generate realistic 3D human-object interactions (HOIs) by posing a skinned human model to interact with objects based on text descriptions. It uses text-to-image diffusion models to create diverse interactions without needing large datasets.

And talking about Splats, Feature Splatting can manipulate both the appearance and the physical properties of objects in a 3D scene using text prompts.

Tailor3D can create customized 3D assets from text or single and dual-side images. The method also supports adding changes to the inputs through additional text prompts.

DIRECTOR can generate complex camera trajectories from text that describe the relation and synchronization between the camera and characters.

Director3D can generate real-world 3D scenes and adaptive camera trajectories from text prompts. The method is able to generate pixel-aligned 3D Gaussians as an immediate 3D scene representation for consistent denoising.

GradeADreamer is yet another text-to-3D method. This one is capable of producing high-quality assets with a total generation time of under 30 minutes using only a single RTX 3090 GPU.

Dual3D is yet another text-to-3D method that can generate high-quality 3D assets from text prompts in only 1 minute.

X-Oscar can generate high-quality 3D avatars from text prompts. It uses a step-by-step process for geometry, texture, and animation, while addressing issues like low quality and oversaturation through advanced techniques.

GaussianCube is a image-to-3D model that is able to generate high-quality 3D objects from multi-view images. This one also uses 3D Gaussian Splatting, converts the unstructured representation into a structured voxel grid, and then trains a 3D diffusion model to generate new objects.

Garment3DGen can stylize the geometry and textures from 2D image and 3D mesh garments! These can be fitted on top of parametric bodies and simulated. Could be used for hand-garment interaction in VR or to turn sketches into 3D garments.

TexDreamer can generate high-quality 3D human textures from text and images. It uses a smart fine-tuning method and a unique translator module to create realistic textures quickly while keeping important details intact.

HoloDreamer can generate enclosed 3D scenes from text descriptions. It does so by first creating a high-quality equirectangular panorama and then rapidly reconstructing the 3D scene using 3D Gaussian Splatting.

Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting can create high-quality 3D content from text prompts. It uses edge, depth, normal, and scribble maps in a multi-view diffusion model, enhancing 3D shapes with a unique hybrid guidance method.

ViewDiff is a method that can generate high-quality, multi-view consistent images of a real-world 3D object in authentic surroundings from a single text prompt or a single posed image.

MeshFormer can generate high-quality 3D textured meshes from just a few 2D images in seconds.

GALA3D is a text-to-3D method that can generate complex scenes with multiple objects and control their placement and interaction. The method uses large language models to generate initial layout descriptions and then optimizes the 3D scene with conditioned diffusion to make it more realistic.

LGM can generate high-resolution 3D models from text prompts or single-view images. It uses a fast multi-view Gaussian representation, producing models in under 5 seconds while maintaining high quality.

AToM is a text-to-mesh framework that can generate high-quality textured 3D meshes from text prompts in less than a second. The method is optimized across multiple prompts and is able to create diverse objects for which it wasn’t trained on.