Deformable GANs for Pose-based Human Image Generation (1801.00055v2)

Abstract: In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons in different poses and we compare our method with previous work in this area showing state-of-the-art results in two benchmarks. Our method can be applied to the wider field of deformable object generation, provided that the pose of the articulated object can be extracted using a keypoint detector.

• The paper proposes deformable skip connections that effectively handle pose-induced pixel misalignments in image generation.
• It introduces a nearest-neighbour loss to preserve fine details, outperforming traditional L1 and L2 loss functions.
• Performance evaluations on benchmark datasets demonstrate superior qualitative and quantitative results for real-world applications.

Deformable GANs for Pose-based Human Image Generation

The paper "Deformable GANs for Pose-based Human Image Generation" addresses a significant problem in computer vision: generating human images conditioned on specified poses. The research introduces a method using Generative Adversarial Networks (GANs), which facilitates the synthesis of human images in novel poses while preserving detailed appearance characteristics from the source image.

Key Contributions

The authors propose an innovative approach by utilizing deformable skip connections within the GAN architecture. This mechanism effectively handles pixel-to-pixel misalignments resulting from pose variations. In addition to the architectural innovation, a nearest-neighbour loss is introduced, replacing traditional $L_1$ and $L_2$ losses. This new loss function focuses on aligning generated images with the target pose image at a more detailed level, accommodating minor spatial distortions without significant penalization.

Performance Evaluation

The proposed model is evaluated against state-of-the-art techniques using benchmark datasets, showing superior performance in terms of qualitative and quantitative metrics. The approach demonstrates improved detail preservation in generated images compared to conventional methods that struggle with significant pose-induced misalignment.

Implications and Future Directions

The introduced deformable skip connections significantly advance the task of pose-based image generation, showcasing an improved maintenance of fine-level details through spatial transformations. This development holds promise for numerous applications, extending beyond human images to other articulated objects such as animals or machinery, provided the pose can be adequately captured using keypoint detectors.

The practical implications of this work include advancements in fields such as computer graphics, digital avatars, and database augmentation for re-identification tasks. Theoretically, it opens discussions for further exploration in non-rigid object deformations and their integration into deep learning models.

Future research might focus on refining these deformable layers, possibly integrating more complex transformation models, or extending the framework to capture even more sophisticated scene dynamics. Investigating the application of similar principles in diverse domains such as 3D model generation or robotics could further harness the potential of deformable GANs. Additionally, the development of these models poses questions about computational efficiency and scalability that warrant further paper.

This research not only contributes a novel method to the domain of generative models but also initiates a dialogue on tackling deformable object generation with deep learning, embedding the proposed approach as a foundation for subsequent innovations in AI and computer vision technologies.