ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on (2012.10495v2)

Abstract: Virtual try-on has garnered interest as a neural rendering benchmark task to evaluate complex object transfer and scene composition. Recent works in virtual clothing try-on feature a plethora of possible architectural and data representation choices. However, they present little clarity on quantifying the isolated visual effect of each choice, nor do they specify the hyperparameter details that are key to experimental reproduction. Our work, ShineOn, approaches the try-on task from a bottom-up approach and aims to shine light on the visual and quantitative effects of each experiment. We build a series of scientific experiments to isolate effective design choices in video synthesis for virtual clothing try-on. Specifically, we investigate the effect of different pose annotations, self-attention layer placement, and activation functions on the quantitative and qualitative performance of video virtual try-on. We find that DensePose annotations not only enhance face details but also decrease memory usage and training time. Next, we find that attention layers improve face and neck quality. Finally, we show that GELU and ReLU activation functions are the most effective in our experiments despite the appeal of newer activations such as Swish and Sine. We will release a well-organized code base, hyperparameters, and model checkpoints to support the reproducibility of our results. We expect our extensive experiments and code to greatly inform future design choices in video virtual try-on. Our code may be accessed at https://github.com/andrewjong/ShineOn-Virtual-Tryon.

• The paper demonstrates that DensePose significantly improves video virtual try-on by preserving facial details and reducing computational resources.
• It reveals that self-attention layers yield marginal visual improvements, underscoring the role of spatial attention in enhancing synthesis quality.
• The analysis shows that GELU and ReLU outperform newer activation functions and that optical flow may degrade temporal consistency due to artifacts.

Analysis of "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on"

The paper "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on" addresses the complexities involved in video-based virtual clothing try-on, a significant aspect within the field of neural rendering and online retail. The authors present a thorough experimental analysis aimed at refining video synthesis for virtual try-on purposes, focusing on key design choices including pose annotations, the incorporation of self-attention layers, and the selection of activation functions. The paper’s methodology is grounded in a series of controlled experiments to isolate the effects of these decisions on both quantitative performance metrics and visual quality.

Contributions and Methodology

The primary contribution of ShineOn lies in its methodical exploration of design choices that enhance the quality and efficiency of video-based virtual try-on systems. The authors initiate their analysis by comparing different types of pose annotations, specifically DensePose and CocoPose. The findings highlight DensePose's superiority in retaining facial details while simultaneously reducing computational resources such as memory usage and training time. This demonstrates the potential of DensePose to streamline processes in video synthesis tasks.

In exploring network architecture improvements, the application of self-attention layers is scrutinized. Although these layers offer only marginal improvements in visual detail concerning face and neck features, they underline the importance of spatial attention mechanisms in complex image synthesis tasks.

Furthermore, the authors assess various activation functions, namely GELU, ReLU, Swish, and Sine. Results indicate that GELU and ReLU facilitate effective try-on synthesis. Despite the theoretical appeal of newer functions such as Swish and Sine in representing high-frequency details, their practical results in this context were less effective.

In an attempt to enhance temporal consistency, the authors experimented with implementing optical flow techniques. However, the attempted improvements led to quality degradation due to introduced artifacts, warranting further investigation into temporal modeling approaches.

Implications and Future Directions

ShineOn sets a precedent for transparent, reproducible research in video virtual try-on technology. By providing an open-source code base along with model checkpoints and hyperparameter specifications, the authors significantly lower the barrier for future research endeavors aimed at refining virtual clothing try-on technologies.

The implications of these findings are twofold: practically, they offer robust guidelines for engineering more efficient and accurate virtual try-on systems that could enhance user experience in e-commerce; theoretically, they provide a foundational understanding of the synergistic effects of various network design choices.

Looking ahead, future research may focus on the persistent challenges identified in this paper, such as the improvement of neck synthesis quality and the integration of global temporal consistency models. Additionally, enhancing cloth warping techniques to incorporate three-dimensional orientations and intricate geometric details could be crucial for advancing the realism of virtual try-on applications.

In summary, ShineOn contributes valuable insights into video-based virtual try-on by systematically examining the impact of focused design choices, setting the stage for further exploration and refinement in this evolving field.