Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis (2011.09055v2)

Abstract: We tackle human image synthesis, including human motion imitation, appearance transfer, and novel view synthesis, within a unified framework. It means that the model, once being trained, can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints to estimate the human body structure. However, they only express the position information with no abilities to characterize the personalized shape of the person and model the limb rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape. It can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose an Attentional Liquid Warping GAN with Attentional Liquid Warping Block (AttLWB) that propagates the source information in both image and feature spaces to the synthesized reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method can support a more flexible warping from multiple sources. To further improve the generalization ability of the unseen source images, a one/few-shot adversarial learning is applied. In detail, it firstly trains a model in an extensive training set. Then, it finetunes the model by one/few-shot unseen image(s) in a self-supervised way to generate high-resolution (512 x 512 and 1024 x 1024) results. Also, we build a new dataset, namely iPER dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our methods in terms of preserving face identity, shape consistency, and clothes details. All codes and dataset are available on https://impersonator.org/work/impersonator-plus-plus.html.

• The paper introduces a unified framework that integrates a 3D body mesh module with an Attentional Liquid Warping GAN to enhance pose and appearance synthesis.
• It employs a novel Attentional Liquid Warping Block for dynamic feature propagation, preserving texture, style, and color details.
• Validation on the iPER dataset demonstrates high-fidelity synthesis with strong metrics like PSNR, SSIM, and LPIPS across varied scenarios.

Insights into "Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis"

The paper entitled "Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis" introduces an ambitious framework that addresses several key tasks in human image synthesis, namely motion imitation, appearance transfer, and novel view synthesis. Leveraging a single, unified model, the authors aim to surpass task-specific models that often fall short in capturing the intricacies of human poses and appearances.

Framework Overview and Contributions

The primary innovation of this work is the integration of a 3D body mesh recovery module with an Attentional Liquid Warping GAN (Generative Adversarial Network). The body mesh module facilitates disentangling of pose and shape, offering advancements over traditional 2D keypoint methods by modeling joint locations, rotations, and the personalized shape of the human body. Such modeling allows the framework to maintain fidelity in both pose and appearance transformations.

The Attentional Liquid Warping GAN introduces the Attentional Liquid Warping Block (AttLWB) for dynamic feature propagations, an evolution that advances upon conventional warping methods. The AttLWB integrates source feature information effectively into target feature spaces, enhancing the conceptual synthesis abilities of the network. Through convolutional auto-encoder-derived features, this strategy preserves essential textural, stylistic, and color details.

An additional contribution is the introduction of one/few-shot learning paradigms to improve the framework's adaptability to unseen inputs, which holds promise for high-fidelity results across variations in identities and clothing styles. The authors further validate their approach with the iPER dataset, specifically curated for these tasks, showcasing strong results in maintaining face identity, shape consistency, and clothing detail integrity.

Numerical Findings

The results presented in the paper demonstrate that the method achieves notable fidelity in human image synthesis, evident through metrics like PSNR, SSIM, and LPIPS. The use of a diverse dataset underscores its robustness across a wide array of scenarios, reinforcing its applicability for generalized human image synthesis tasks.

Practical and Theoretical Implications

Theoretically, this framework challenges existing paradigms by suggesting robust 3D integration in generative tasks could resolve prevalent issues associated with 2D approaches, particularly in maintaining authenticity of human shapes and motions. Practically, such advancements could impact areas like virtual reality and digital media production, where realistic human depictions are invaluable.

Future Directions

Considering the framework’s promising performance, future research could further explore enhancements in real-time applicability and handling of highly intricate clothing and extreme poses. Enhancements in modeling hands and facial expressions may also bridge current gaps.

Furthermore, exploring multi-view synthesis applications and addressing the constraints of static backgrounds as mentioned in the limitations could expand the framework's versatility. Enhanced understanding of how adversarial methods within this framework scale with increasing data diversity remains a key avenue for exploration.

In conclusion, the "Liquid Warping GAN with Attention" presents a solid amalgamation of innovation both in methodology and practical application, setting a new trajectory for future research in human-centric image synthesis.