REVIEW 6 cited by
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
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
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization pipeline: the first stage optimizes a neural radiance field by incorporating constraints from the reference image at the frontal view and diffusion prior at novel views; the second stage transforms the coarse model into textured point clouds and further elevates the realism with diffusion prior while leveraging the high-quality textures from the reference image. Extensive experiments demonstrate that our method outperforms prior works by a large margin, resulting in faithful reconstructions and impressive visual quality. Our method presents the first attempt to achieve high-quality 3D creation from a single image for general objects and enables various applications such as text-to-3D creation and texture editing.
Forward citations
Cited by 6 Pith papers
-
AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
A feed-forward VAE plus rectified-flow model animates arbitrary static meshes from text prompts in seconds, with a new 4M-sequence training dataset.
-
PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
A multi-view diffusion pipeline that segments 3D objects into parts, completes occluded or invisible parts, and reconstructs them into a compositional 3D asset.
-
LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors
LiftImage3D generates small-motion video clips from one image, registers them with MASt3R, and fits a distortion-aware 3D Gaussian field whose canonical scene renders new views.
-
AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers
AC3D improves camera control in video diffusion transformers by conditioning only early denoising steps and the first 8 of 32 blocks, and by adding 20K static-camera dynamic videos to training.
-
PaintScene4D: Consistent 4D Scene Generation from Text Prompts
A training-free pipeline that turns one text-to-video clip into a multi-view 4D scene renderable along user-chosen camera paths.
-
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.