BrushEdit: Interactive Editing Paradigms

• BrushEdit is a family of interactive editing paradigms that enable free-form, semantic, and region-specific modifications using brush-style inputs.
• It integrates advanced generative and discriminative models to deliver localized, high-fidelity edits and intuitive style control.
• Applications include interactive image editing and 3D sculpting, empowering users with real-time feedback and precise control over visual content.

BrushEdit refers to a family of interactive editing paradigms that enable users to perform free-form, region-specific, and semantically guided modifications to images or 3D content using "brush"-style user inputs. These methods, developed across multiple vision and graphics subdomains, center around painting, sketching, or masking interfaces—integrated with advanced generative and discriminative models—to facilitate localized and interpretable visual editing, often with the aid of natural language or reference data. The defining feature of these approaches is direct, spatially explicit region targeting (via brush, scribble, or stroke), combined with high-level model-guided synthesis or deformation, yielding controllable yet high-fidelity edits for both images and 3D scenes.

1. Conceptual Foundations and Motivations

BrushEdit advances the state of region-based and semantic visual editing by overcoming core limitations of earlier inversion-based (Li et al., 2024), instruction-based (Li et al., 2024), or sketch-to-image (Yang et al., 2020) frameworks:

• Localized Editing: Direct region targeting via user-drawn brush masks or strokes, as opposed to implicit global prompt-based modification.
• Semantic and Style Control: Integration of language-driven instructions or reference images to determine "what" to insert, modify, or synthesize within designated areas (Li et al., 2024, Chu et al., 28 Feb 2025, Xu et al., 26 May 2025).
• Interactive and Intuitive Interfaces: User-centric tools offering real-time feedback, brush strength/intensity controls, layering, mask refinement, and optional multi-turn correction (Gholami et al., 2024, Li et al., 2024).
• Model Efficiency and Flexibility: Emphasis on training-free manipulation for inference-time compositionality (e.g. via energy-based guidance, latent noise injection, or test-time attention manipulation) (Gholami et al., 2024, Chu et al., 28 Feb 2025, Xu et al., 26 May 2025).

These principles enable non-expert users and professionals alike to execute complex edits (addition, removal, style transfer, texture synthesis, 3D sculpting) in a workflow analogous to traditional digital painting or sculpting, but powered by state-of-the-art generative models.

2. Algorithmic Variants and Representative Architectures

BrushEdit instantiations span both 2D (image) and 3D (shape/texture) domains, leveraging diverse model architectures.

2D Image Editing

System	Core Model/Technique	UI Paradigm
BrushEdit (Li et al., 2024)	MLLM + dual-branch diffusion inpainting	Free-form brush, natural language
DiffBrush (Chu et al., 28 Feb 2025)	Energy-guided latent diffusion	Layered color blobs, per-instance mask
Layered Diffusion Brushes (Gholami et al., 2024)	LDM with real-time masked noise injection	Stackable brush layers, mask & prompt

• Instruction-Based Inpainting: BrushEdit (Li et al., 2024) orchestrates an agent-based system coupling an Editing Instructor (MLLM plus object detector for region proposal and semantic understanding) with an Editing Conductor (dual-branch UNet for diffusion inpainting), supporting both automatic and user-refined masks, and region-specific language-generated targets.
• Guided Diffusion: DiffBrush (Chu et al., 28 Feb 2025) operates on any pretrained text-to-image diffusion model and applies three guidance mechanisms at inference: (1) latent color matching, (2) instance-specific attention map reweighting, and (3) latent regeneration to initialize the sample in alignment with the user's mask and stroke specification.
• Layered Editing: Layered Diffusion Brushes (Gholami et al., 2024) enables region-specific denoising via masked random noise injection at intermediate diffusion steps, with per-edit prompts and stackable layer management for iterative or parallel edits.

3D Shape and Texture Editing

System	Domain	Strategy
3D PixBrush (Decatur et al., 4 Jul 2025)	3D texture synthesis	Neural field UV masking, 2D SDS + LMIG
INST-Sculpt (Rubab et al., 5 Feb 2025)	Neural SDF sculpting	Tubular brush, stroke-based MLP finetuning

• 3D PixBrush (Decatur et al., 4 Jul 2025): Predicts localized masks and textures from image+text references on UV-parameterized meshes using score distillation sampling and localization-modulated image guidance (LMIG), providing globally semantic and locally precise placement and style transfer.
• Stroke-Based Sculpting (Rubab et al., 5 Feb 2025): Employs user-drawn 3D strokes to define tubular editing neighborhoods on neural SDFs, with brush profiles dictating spatial deformation fields and batched MLP parameter updates under strict regularization.

3. Mathematical Formalization and Pipeline Workflow

BrushEdit frameworks manifest diverse mathematical strategies tailored to their task and representation.

Diffusion-Guided Editing (Image Domain)

• Localized Noise Injection: For a mask $m$ and cached latent $Z_r$, brush initialization is

$Z'_0 = Z_r + \alpha \cdot (Z'_0 \circ m)$

where $Z'_0 \sim \mathcal{N}(0, S'^2 I)$ and $\alpha$ is user- or size-scaled brush strength (Gholami et al., 2024).

• Guided Denoising: Modified noise predictions during reverse diffusion, e.g.

$$\epsilon_{\text{guided}} = \epsilon_\theta(z_t; t, c) + G_{\text{CL}} + G_{\text{IS}}$$

with

$$G_{\text{CL}} = -2 s_{\text{cl}}\sigma_t (z_t - z_t^p)$$

and $G_{\text{IS}}$ from attention-based hinge energies (Chu et al., 28 Feb 2025).

• Inpainting with Preservation: Dual-branch networks fuse pre- and post-mask latents, enforcing

$$\epsilon_\theta(z_t, t, T')_i \leftarrow \epsilon_\theta(z_t, t, T')_i + w \cdot Z(\epsilon_\theta^{\text{BrushNet}}([\cdot])_i)$$

for each UNet layer (Li et al., 2024).

3D Neural Editing

• Stroke-based SDF Modification: Given edit samples $S_{\text{edit}}$ along a brush stroke and desired normal offsets $\Delta d_{ij}$,

$$\mathcal{L}_{\mathrm{edit}}(\Delta\theta) = \sum_{i,j} w_{ij} \left[ f_{\theta+\Delta\theta}(p_{ij}) - (f_\theta(p_{ij}) + \Delta d_{ij}) \right]^2 + \lambda_{\rm reg} \|\Delta\theta\|^2$$

with untouched region regularization enforcing eikonal and binding constraints (Rubab et al., 5 Feb 2025).

In-Context Learning for Visual Insertion

• Demo-guided Attention Manipulation: At each attention head, feature shifting and head-wise reweighting adjust the latent update as:

$$\hat{h}_s = h_s + \alpha_1 (A_{s,p} v_p) + \alpha_2 (A_{s,c} v_c)$$

then

$$\hat{h}_s^{(h)} \gets \hat{h}_s^{(h)} \cdot \hat{V}_h$$

with prompt- and content-aligned attention blocks and normalized head weights (Xu et al., 26 May 2025).

4. Quantitative Evaluation and Comparative Analysis

Evaluation protocols span standard low-level and perceptual metrics, as well as user studies and task completion efficiency.

Method	PSNR↑	LPIPS↓	SSIM↑	CLIP Sim↑	Time (s)	User Pref.
BrushEdit (Li et al., 2024)	32.16	0.0172	0.970	0.224	3.6	Highest
DiffBrush (Chu et al., 28 Feb 2025)	–	0.738*	–	0.326	–	8.3/10
Layered Diff. Brushes	–	–	–	–	0.14	80.4% SUS
SD-Inpainting/BLD	~21.5	0.048	~0.89	0.262	>3.6	Lower

*LPIPS↑ as a structure preservation measure (DiffBrush).

• Localization Accuracy: 3D PixBrush achieves IoU≈0.82 for mask prediction, with substantial improvements over text-only methods (Decatur et al., 4 Jul 2025).
• Task Efficiency: Layered Diffusion Brushes achieves ∼140 ms per 512×512 image edit (Gholami et al., 2024).
• Region and Content Fidelity: BrushEdit (Li et al., 2024) yields superior PSNR, LPIPS, and SSIM, as well as stronger background preservation compared to traditional inversion/instruction methods.

5. Applications and Workflows

BrushEdit systems are deployed in a range of creative and technical workflows:

• Free-form Image Editing: Semantic object addition, removal, attribute change, background alteration, error correction, region-specific style transfer (Li et al., 2024, Gholami et al., 2024, Chu et al., 28 Feb 2025).
• Interactive 3D Operations: Texture decal synthesis with reference transfer (Decatur et al., 4 Jul 2025) and neural implicit surface sculpting via stroke-defined tubular deformations (Rubab et al., 5 Feb 2025).
• Instruction-Driven and Multi-Modal Editing: Integration of MLLMs for natural-language-driven, category-aware region proposal and iterative multimodal editing (Li et al., 2024).
• Zero-shot Customization and Data Augmentation: Test-time, demonstration-guided object insertion without model retraining (Xu et al., 26 May 2025).

Characteristic pipelines support user-drawn mask refinement, slider-based parameterization (e.g. refinement or brush strength), stacking or reordering of edits, and real-time iterative feedback (Li et al., 2024, Gholami et al., 2024).

6. Limitations and Ongoing Research Directions

Despite substantial progress, BrushEdit methodologies confront several enduring challenges:

• Structural and Semantic Limitations: Large structural alterations and complex textures can exceed the capacity of pretrained latent generators, especially under high mask irregularity or for out-of-distribution object classes (Li et al., 2024, Chu et al., 28 Feb 2025).
• Mask and Guide Proposal Accuracy: Mask acquisition (e.g., MLLMs + detector) may misclassify editing type or object, requiring user correction or more robust vision-language grounding (Li et al., 2024).
• UI and User Experience Limitations: Current implementations may lack features such as advanced blending modes, undo/redo history, or ergonomic brush tooling (Gholami et al., 2024).
• Hyperparameter Sensitivity: Several frameworks require manual adjustment of correlated parameters (e.g., brush strength, reverse steps), impacting rapid exploration (Gholami et al., 2024, Chu et al., 28 Feb 2025).

Proposed research extensions include enhanced region-aware multimodal LLMs, uncertainty-aware boundary blending, collaborative editing support, and extension to temporally consistent video scenarios (Li et al., 2024, Gholami et al., 2024, Decatur et al., 4 Jul 2025).

7. Impact and Significance in Visual Computing

BrushEdit systems unify multimodal instruction, interpretable region selection, and model-driven content synthesis, enabling unprecedented control over high-fidelity visual editing for research, professional, and creative communities. By designing pipelines that balance semantic expressivity, spatial targeting, and real-time feedback, BrushEdit has established a new standard for plug-and-play, flexible, and user-oriented editing frameworks across both 2D and 3D settings (Li et al., 2024, Chu et al., 28 Feb 2025, Gholami et al., 2024, Xu et al., 26 May 2025, Decatur et al., 4 Jul 2025, Rubab et al., 5 Feb 2025). The paradigm's methodological innovations—such as inference-time brush guidance, localization-oriented attention manipulation, and dual-branch inpainting—are now foundational in the development of next-generation interactive visual content creation tools.