UniHuman: A Unified Model for Diverse In-the-Wild Human Image Editing

Centrala begrepp

UniHuman is a unified model that addresses multiple facets of human image editing, including reposing, virtual try-on, and text-based manipulation, achieving high-quality results across diverse real-world settings.

Sammanfattning

The paper proposes UniHuman, a unified model that addresses multiple human image editing tasks, including reposing, virtual try-on, and text-based manipulation. The key highlights are:

UniHuman leverages the synergies between related tasks, such as reposing and virtual try-on, to mutually reinforce the model's performance. It introduces a pose-warping module that can handle unseen textures and patterns, enhancing the model's generalization capacity.

To adapt the model to real-world scenarios, the authors curated a large-scale dataset, LH-400K, with diverse human images encompassing a wide range of poses, backgrounds, and age groups. This dataset, combined with existing datasets, enables the model to better handle in-the-wild cases.

Extensive experiments on both in-domain and out-of-domain test sets demonstrate that UniHuman outperforms task-specific models quantitatively and qualitatively. In user studies, UniHuman is preferred by users in an average of 77% of cases.

The model can effectively perform various human image editing tasks, such as changing a person's pose, fitting new clothing, and manipulating the image based on text prompts, while preserving the identity and texture details of the original person.

Statistik

The proposed LH-400K dataset contains 409,270 high-quality single-human images with diverse backgrounds, age groups, and body shapes.
The WPose dataset contains 2,304 real-world human image pairs with diverse postures and backgrounds for evaluating the model's generalization on reposing.
The WVTON dataset contains 440 test pairs with garment images from Stock photos, including diverse graphic patterns and fabric textures, for evaluating the model's generalization on virtual try-on.

Citat

"UniHuman learns informative representations by leveraging multiple data sources and connections between related tasks, achieving high-quality results across various human image editing objectives."
"Our model takes a step further by exploiting the relationship between reposing and virtual try-on. Specifically, reposing requires modifying the pose of all body parts and clothing items, while virtual try-on only adapts the pose of the target garment."
"The introduced pose-warping module can explicitly leverage both dense and sparse pose correspondences to obtain visible pixels on all three tasks, equipping it with the capacity to handle previously unseen textures and patterns."

Viktiga insikter från

UniHuman

by Nannan Li,Qi... på arxiv.org 04-02-2024

https://arxiv.org/pdf/2312.14985.pdf

Djupare frågor

How can the proposed unified model be extended to handle human image editing in the video domain

To extend the proposed unified model to handle human image editing in the video domain, several key considerations need to be taken into account. Firstly, the model would need to incorporate temporal information to account for the dynamic nature of videos. This could involve utilizing recurrent neural networks or temporal convolutional networks to process sequential frames efficiently. Additionally, the model would need to maintain consistency across frames to ensure smooth transitions between edited frames. Techniques such as optical flow estimation could be used to align poses and textures across frames. Moreover, the model could benefit from incorporating motion prediction to anticipate changes in poses and clothing across frames. By integrating these elements, the unified model can be adapted to handle human image editing in the video domain effectively.

What are the potential limitations of the pose-warping module, and how could it be further improved to handle more complex occlusions and deformations

The pose-warping module, while effective in handling pose and garment changes, may have limitations when faced with more complex occlusions and deformations. One potential limitation is the module's reliance on pose detectors, which may struggle with occluded body parts or complex poses. To address this, the module could be enhanced by incorporating advanced pose estimation techniques that are robust to occlusions and deformations, such as graph-based pose estimation models. Additionally, the module could benefit from integrating 3D human representations to better handle complex spatial relationships and occlusions. By leveraging multi-view information, the module can generate more accurate pose-warping results, even in challenging scenarios with occlusions and deformations.

Given the diverse dataset collected, how could the model's performance be further enhanced by incorporating additional modalities, such as 3D human representations or multi-view information

Incorporating additional modalities, such as 3D human representations or multi-view information, can significantly enhance the model's performance with the diverse dataset collected. By integrating 3D human representations, the model can better capture the spatial relationships between body parts and clothing, leading to more accurate pose-warping and texture transfer. Multi-view information can provide complementary perspectives on the same scene, enabling the model to generate more realistic and consistent results across different viewpoints. Furthermore, leveraging 3D human representations can facilitate the generation of more realistic and detailed textures, enhancing the overall quality of the edited images. By combining these modalities, the model can achieve a more comprehensive understanding of human images and improve its performance on diverse and challenging editing tasks.