REVIEW 7 cited by
Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Automatic 3D content creation has achieved rapid progress recently due to the availability of pre-trained, large language models and image diffusion models, forming the emerging topic of text-to-3D content creation. Existing text-to-3D methods commonly use implicit scene representations, which couple the geometry and appearance via volume rendering and are suboptimal in terms of recovering finer geometries and achieving photorealistic rendering; consequently, they are less effective for generating high-quality 3D assets. In this work, we propose a new method of Fantasia3D for high-quality text-to-3D content creation. Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance. For geometry learning, we rely on a hybrid scene representation, and propose to encode surface normal extracted from the representation as the input of the image diffusion model. For appearance modeling, we introduce the spatially varying bidirectional reflectance distribution function (BRDF) into the text-to-3D task, and learn the surface material for photorealistic rendering of the generated surface. Our disentangled framework is more compatible with popular graphics engines, supporting relighting, editing, and physical simulation of the generated 3D assets. We conduct thorough experiments that show the advantages of our method over existing ones under different text-to-3D task settings. Project page and source codes: https://fantasia3d.github.io/.
Forward citations
Cited by 7 Pith papers
-
3D PixBrush: Image-Guided Local Texture Synthesis
A method that uses a reference image to automatically predict a localization mask and synthesize a matching local texture on a 3D mesh.
-
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
A video-to-4D model that encodes mesh animations into compact Gaussian variation latents and diffuses them conditioned on the video and a canonical Gaussian splat.
-
Matrix3D: Large Photogrammetry Model All-in-One
A single multi-modal diffusion transformer trained with masked learning performs pose estimation, depth prediction, and novel view synthesis in one model, reporting SOTA pose and NVS numbers.
-
Few-step Flow for 3D Generation via Marginal-Data Transport Distillation
MDT-dist distills a pretrained 3D flow model into a 1-2 step generator using velocity matching plus velocity distillation, cutting TRELLIS inference from 6.1s to 0.68s while approximately preserving generation quality.
-
ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding
ShapeLLM-Omni unifies text, image, and 3D generation and understanding in one autoregressive LLM using discrete 3D tokens and a new 3D-Alpaca training dataset.
-
DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation
A pipeline that generates editable 3D scenes from natural language by combining LLM-based layout planning, multi-timestep diffusion distillation, and staged camera sampling.
-
DreamComposer++: Empowering Diffusion Models with Multi-View Conditions for 3D Content Generation
A multi-view conditioning framework that improves controllable novel view synthesis and 3D reconstruction by injecting fused 3D latents into frozen image and video diffusion models.
Discussion (0). Continue with ORCID to comment.