Shape-guided Object Inpainting (2204.07845v1)

Abstract: Previous works on image inpainting mainly focus on inpainting background or partially missing objects, while the problem of inpainting an entire missing object remains unexplored. This work studies a new image inpainting task, i.e. shape-guided object inpainting. Given an incomplete input image, the goal is to fill in the hole by generating an object based on the context and implicit guidance given by the hole shape. Since previous methods for image inpainting are mainly designed for background inpainting, they are not suitable for this task. Therefore, we propose a new data preparation method and a novel Contextual Object Generator (CogNet) for the object inpainting task. On the data side, we incorporate object priors into training data by using object instances as holes. The CogNet has a two-stream architecture that combines the standard bottom-up image completion process with a top-down object generation process. A predictive class embedding module bridges the two streams by predicting the class of the missing object from the bottom-up features, from which a semantic object map is derived as the input of the top-down stream. Experiments demonstrate that the proposed method can generate realistic objects that fit the context in terms of both visual appearance and semantic meanings. Code can be found at the project page \url{https://zengxianyu.github.io/objpaint}

• The paper introduces a novel inpainting task focused on generating entire objects using implicit shape cues.
• It employs the CogNet architecture, combining bottom-up context propagation with top-down semantic object generation using predictive class embeddings.
• Experimental results on COCO, Cityscapes, and Places2 confirm superior performance via improved FID and LPIPS metrics.

Shape-guided Object Inpainting: Expanding Image Completion Capabilities

The paper introduces a novel approach to image inpainting, termed "shape-guided object inpainting," which addresses the challenge of generating missing objects within an image contextually. This task differs from traditional image inpainting that predominantly focuses on background reconstruction or partially missing objects. The motivation behind this research is to generate complete objects in the missing image regions, driven by understanding the object shape and leveraging the surrounding context to ensure coherence and realism in the generated results.

Core Contributions and Methodology

The research makes several contributions to the domain of computer vision and image inpainting:

1. Novel Task Definition: The paper defines a new inpainting challenge that goes beyond filling in backgrounds or partially occluded objects. The task involves generating whole objects in the missing regions, inspired by the implicit shape cues present in the incomplete image.
2. Data Preparation: A significant aspect of this work is the preparation of a training dataset that emphasizes the generation of entire objects. The authors employ object instances as holes during the training phase, integrating object priors directly into the learning process. This contrasts with traditional methods that often use arbitrary-shaped masks and thereby risk a bias towards background generation.
3. Contextual Object Generator (CogNet): The authors propose CogNet, an innovative architecture for the object inpainting task. CogNet features a two-stream network design combining a bottom-up image completion process with a top-down object generation paradigm:
   • Bottom-up Stream: This component mirrors classical inpainting methods, focusing on visual coherence and context propagation from known regions into missing areas.
   • Top-down Stream: In contrast, this stream draws inspiration from semantic image synthesis, emphasizing the generation of semantically meaningful objects. It employs a predictive class embedding (PCE) mechanism to incorporate object class information derived from contextual background features.
4. Architectural Innovation: The two-stream architecture allows for effective integration of contextual and semantic information, meeting the dual objectives of visual coherence and object fidelity. Furthermore, the SC AdaIN module enhances the model’s capacity to generate diverse and realistic object appearances by modulating feature maps spatially and channel-wise, contingent on predicted class embeddings and random latent vectors.

Experimental Outcomes and Implications

The experiments demonstrate the proficiency of the proposed method on several datasets including COCO, Cityscapes, and Places2. Quantitative metrics like FID (Fréchet Inception Distance) and LPIPS (Learned Perceptual Image Patch Similarity) underscore the superior performance of the proposed approach over state-of-the-art models, such as DeepFillV2 and CoModGAN. The generated objects not only possess visual congruence with the input but are also semantically coherent within the scene context, validating the efficacy of the shape-guided methodology.

The proposed approach offers implications for practical applications such as object re-generation, anonymization, and content creation in image editing. Theoretically, it bridges the gap between context-driven inpainting and object-specific generation, offering a unified framework for semantic restoration tasks.

Future Directions

There are several promising avenues for future exploration. Enhancing the architectural complexity of CogNet could further improve class prediction accuracy and object realism. Integrating more sophisticated class recognition models would likely enhance the semantic accuracy of generated objects. Additionally, extending this framework to 3D object inpainting or video sequences could offer substantial benefits for dynamic environments and real-time applications.

The shape-guided object inpainting approach presented in this paper marks a valuable progression in the field of image generation and computer vision, promising advancements both in theoretical understanding and application domains. As object inpainting becomes increasingly relevant in AI applications, such innovations stand to significantly augment the capability of generative models in comprehending and reconstructing visual information.