Naturalistic Inpainting for Context Enhancement (NICE)

Updated 4 December 2025

NICE is a framework that integrates generative models, contextual attention, and segmentation to achieve photorealistic inpainting while preserving scene context.
It employs multi-stage pipelines such as YOLOv3-based mask generation and GAN/diffusion inpainting to remove distractors and enhance image restoration.
Empirical outcomes show improved robustness in vision-language tasks and robotic manipulation, with notable gains in spatial affordance prediction and manipulation accuracy.

Naturalistic Inpainting for Context Enhancement (NICE) refers to a set of frameworks and methodologies for augmenting image data—typically for the purpose of contextual enhancement—by digitally removing, replacing, or restyling regions within a photograph while maintaining scene fidelity and action-label consistency. A NICE system leverages state-of-the-art generative models (e.g., GANs, diffusion models), contextual attention, and segmentation modules to produce semantically plausible and photorealistic completions of masked areas, thereby improving downstream vision-language and manipulator robustness while eliminating distracting content. Recent research covers pipelines based on adversarial learning, diffusion-based inpainting, and multi-action data augmentation, with applications ranging from image restoration to robot policy improvement (Darapaneni et al., 2022, Gebre et al., 24 Mar 2024, Pakdamansavoji et al., 27 Nov 2025).

1. NICE Workflow Architectures

NICE architectures are distinguished by their multi-step pipelines that automate region detection, mask generation, and context-preserving inpainting. The following table summarizes the principal workflow stages in canonical NICE systems described in recent literature:

Pipeline Name	Mask Generation	Inpainting Core	Resolution Enhancement
Contextual Attention GAN (Darapaneni et al., 2022)	YOLOv3 bounding boxes	Coarse-to-fine GAN + Contextual Attention	SRGAN Super-Resolution
Diffusion-based NICE (Gebre et al., 24 Mar 2024)	Manual or buffered masks	DDPM + RePaint mask-aware sampling + convex blend	None
NICE Scene Surgery (Pakdamansavoji et al., 27 Nov 2025)	SAM-2 segmentations	LaMa, Stable Diffusion, Restyle operator	None

In (Darapaneni et al., 2022), the system proceeds through YOLOv3-based object detection, binary mask formation, a two-stage GAN (coarse and refine generators, global and local discriminators), and upscaling via SRGAN. In (Gebre et al., 24 Mar 2024), a diffusion model implements mask-aware sampling, context injection, and target blending without retraining. In (Pakdamansavoji et al., 27 Nov 2025), segmentation masks enable operations such as distractor removal, restyling (using external texture datasets), and object replacement (with LLM-driven prompts and latent diffusion inpainting).

2. Mask Creation and Object Selection

Mask generation is foundational to NICE pipelines, as it defines the regions for inpainting and augmentation. Detection strategies vary:

YOLOv3 detection: Divides the image into an S×S grid; objectness score and class are predicted. Selected boxes (confidence ≥ 0.5, IoU ≤ 0.4 post-NMS) are mapped to binary masks where $m(p)=0$ for pixels inside target boxes (Darapaneni et al., 2022).
Vision-Language Segmentation: Florence-2 VLM provides object bounding boxes and class labels. SAM-2 refines these into segmentation masks. Target objects are matched to the human instruction, retaining distractors as candidates for NICE surgery (Pakdamansavoji et al., 27 Nov 2025).
Manual or buffered masks: In the diffusion-based approach, masks are manually constructed or expanded ("heated"/buffered) to handle ambiguous regions (Gebre et al., 24 Mar 2024).

This role assignment ensures that target objects remain unaltered, while distractors are selected for removal, restyling, or replacement, preserving spatial and semantic relationships necessary for robust policy learning and affordance prediction.

3. Inpainting Core Mechanisms

NICE systems leverage advanced inpainting backbones tailored for realistic context propagation:

Coarse-to-Fine GAN with Contextual Attention (Darapaneni et al., 2022): Two parallel generator networks operate at different stages. The refinement generator (G₂) uses a contextual attention module, reconstructing missing patches by weighted summation of background features:

$y_i = \sum_j s_{ij} b_j$

where $s_{ij}$ is the softmax-normalized similarity between foreground and background features.

Diffusion Model with RePaint and Target Conditioning (Gebre et al., 24 Mar 2024): The DDPM backbone (U-Net) applies mask-aware sampling, maintaining the noisy context outside the mask and employing convex blending in masked regions:

$x_{t-1}^{unk} = \lambda_t x_{repaint, t-1} + (1 - \lambda_t) x_{target, t-1}$

The workflow implements resampling "jumps" for smooth transitions along the mask boundary.

LaMa and Stable Diffusion (Pakdamansavoji et al., 27 Nov 2025): Image-space operations use LaMa for large-hole inpainting and Stable Diffusion for prompt-driven semantic object insertion, guided by zero-shot textual cues from an LLM. Restyling is performed using alpha-blended texture patches, controlling for illumination and surface cues.

4. Context Consistency and Resolution Preservation

Preservation of global scene structure and local texture is central to NICE. Technical approaches include:

Dual Discriminator Adversarial Losses (Darapaneni et al., 2022): A global discriminator enforces overall realism, while the local discriminator penalizes boundary artifacts in the inpainted region. Spatially discounted L₁ loss explicitly weights pixels close to the mask boundary.
Exact Context Injection via Diffusion (Gebre et al., 24 Mar 2024): The unmasked pixels outside the editing region are never altered; stochastic resampling is restricted to the mask, preserving consistency. The convex mixing parameter $\lambda_t$ can be scheduled for adaptive blending.
Action-Label Consistency in Robotics (Pakdamansavoji et al., 27 Nov 2025): NICE modifications do not alter target object pose or obstruct potential grasps, ensuring that expanded data distributions remain compatible with original demonstration trajectories.

For GAN-based systems, super-resolution modules (SRGAN) upscale inpainted patches to original image dimensions, employing perceptual (VGG-based) and adversarial losses to maintain high-frequency details (Darapaneni et al., 2022). Diffusion-based methods operate natively at the test resolution, without the need for explicit upscaling.

5. Quantitative Evaluation, Metrics, and Empirical Outcomes

Performance assessment employs loss metrics relevant to reconstruction fidelity and downstream task improvement:

Image Inpainting Metrics (Darapaneni et al., 2022): $L_1$ reconstruction error (18.9), $L_2$ loss (5.6), peak signal-to-noise ratio (PSNR=16.8 dB), and TV loss (28) are reported. Contextual-attention yields fewer boundary artifacts relative to naïve coarse-to-fine GANs.
Human-Inspected Plausibility (Gebre et al., 24 Mar 2024): Standard metrics (PSNR, LPIPS) are not applied; visual inspection is used to verify that inserted objects faithfully match targets, with boundaries improved via resampling and buffered masks.
Robotic Manipulation Outcomes (Pakdamansavoji et al., 27 Nov 2025): NICE augmentation improved spatial affordance prediction accuracy in highly cluttered scenes by over 20% (from 20.08% to 41.44%), increased manipulation success rates by 11% (SR 64% vs. 53%), and reduced target confusion and collision rates by 6% and 7%, respectively. Ablations demonstrate best results when all three editing operations (removal, restyling, replacement) are mixed.

6. Limitations, Ablations, and Technical Challenges

Several bottlenecks and edge cases are identified:

Large or complex masks: Removing objects occupying >30% of an image may induce texture repetition or distortion artifacts (Darapaneni et al., 2022).
Artifact generation: Diffusion models may produce "creative" hybrids when blend parameters are misconfigured (Gebre et al., 24 Mar 2024). Inpainting artifacts can become evident under extreme lighting or reflective surfaces (Pakdamansavoji et al., 27 Nov 2025).
Scalability of mask construction: Manual mask generation is highlighted as non-scalable; automation via segmentation is a suggested extension (Gebre et al., 24 Mar 2024).
Image-space only edits: NICE currently operates in 2D RGB space; 3D-aware manipulations and trajectory-consistent edits are future research directions (Pakdamansavoji et al., 27 Nov 2025).

Operation-wise ablations reveal that each augmentation (removal, restyle, replacement) separately contributes to robustness, but comprehensive mixing yields superior outcomes. Dilations of masks involve a tradeoff between shadow suppression and background realism.

7. Emerging Directions and Methodological Extensions

Advancing NICE involves:

Integration of free-form mask generation and cross-attention mechanisms: Future systems may employ dynamic scheduling of blend parameters or text-guided inpainting via learned embedding cross-attention (Darapaneni et al., 2022, Gebre et al., 24 Mar 2024).
Automated mask creation via semantic segmentation frameworks: Off-the-shelf detectors can scale NICE to arbitrary scenes, addressing scalability.
3D-aware and trajectory-sensitive edits: Incorporating depth, video, and volumetric reasoning is an explicit direction for long-horizon robotic manipulation (Pakdamansavoji et al., 27 Nov 2025).
Policy-aware generation and closed-loop scene construction: LLM- or affordance-model driven generation may further optimize action-label consistency.

This suggests NICE methodologies are converging across perceptual restoration, data augmentation for imitation learning, and generative content control, with diffusion and attention-based networks at the technological forefront.