REVIEW 10 cited by
EditP23: 3D Editing via Propagation of Image Prompts to Multi-View
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
EditP23: 3D Editing via Propagation of Image Prompts to Multi-View
read the original abstract
We present EditP23, a method for mask-free 3D editing that propagates 2D image edits to multi-view representations in a 3D-consistent manner. In contrast to traditional approaches that rely on text-based prompting or explicit spatial masks, EditP23 enables intuitive edits by conditioning on a pair of images: an original view and its user-edited counterpart. These image prompts are used to guide an edit-aware flow in the latent space of a pre-trained multi-view diffusion model, allowing the edit to be coherently propagated across views. Our method operates in a feed-forward manner, without optimization, and preserves the identity of the original object, in both structure and appearance. We demonstrate its effectiveness across a range of object categories and editing scenarios, achieving high fidelity to the source while requiring no manual masks.
Forward citations
Cited by 10 Pith papers
-
VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Image
VecSet-Edit is the first method to perform high-fidelity mesh editing from a single image by analyzing and manipulating spatial token subsets in a pre-trained VecSet LRM.
-
TanGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization
Per-token tangent-space steering, with strength set by velocity-direction mismatch, improves localized training-free 3D editing over global-scaling baselines.
-
EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning
An end-to-end 3D editing framework achieves high-fidelity local edits from coarse bounding boxes and 2D image prompts using region-aware loss reweighting and a large-scale parts-derived training dataset.
-
StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
StreamGVE enables high-quality training-free video editing by converting the task to noise-to-data streaming generation with dual-branch fast sampling, self-attention bridges, cross-attention grounding, source-oriente...
-
StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
StreamEdit enables high-quality training-free video editing by adapting streaming video generation models with dual-branch fast sampling, self-attention bridge, cross-attention grounding, source-oriented guidance, and...
-
Beyond Voxel 3D Editing: Learning from 3D Masks and Self-Constructed Data
BVE framework enables text-guided 3D editing beyond voxel limits by combining self-constructed data, lightweight semantic injection, and annotation-free masking to preserve local invariance.
-
Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes
Introduces dual pose-image representation, cross-modal alignment, and iterative construction to improve prompt alignment and diversity in multi-person text-to-image generation.
-
DataEvolver: Let Your Data Build and Improve Itself via Goal-Driven Loop Agents
DataEvolver introduces a reusable framework with generation-time self-correction and validation-time self-expansion loops that improves visual datasets, shown to outperform baselines on an object-rotation task.
-
VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Image
VecSet-Edit edits a mesh by masking and re-denoising a subset of its VecSet latent tokens, preserving untouched regions better than prior voxel-based editors.
-
SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training
A test-time-trained feedforward model that propagates 2D edits onto 3D Gaussian attributes at interactive speeds.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.