Generating Disentangled 3D Clothed Human Models from Text Descriptions

Core Concepts

This paper presents a novel approach for generating clothing-disentangled 3D human models from text descriptions, enabling high-quality 3D garment generation and supporting clothing editing applications.

Abstract

The paper addresses the task of generating 3D clothed human models from textual descriptions. Previous methods usually encode the human body and clothes as a holistic model, making it difficult to edit the clothing and limiting control over the generation process.
To solve this, the authors propose a layer-wise clothed human representation, where the human body and each clothing item are represented as separate neural radiance fields (NeRFs). They then introduce a progressive optimization strategy to generate the minimal-clothed human body and layer-wise clothes sequentially.
The key technical contributions are:

A novel transparency-based stratified compositional rendering method to prevent penetration between adjacent clothing layers.
Dual SDS losses to supervise the rendered clothed human image and clothes-only image, encouraging the cloth model to decouple from the human body.

The proposed method achieves high-quality disentanglement, enabling applications like virtual try-on and clothing transfer. Extensive experiments demonstrate that TELA outperforms the state-of-the-art holistic modeling methods in terms of clothed human generation quality and supports various clothing editing applications.

Stats

"Given textural descriptions (e.g., "a man wearing jeans, a denim shirt, and a windbreaker"), this paper aims to generate clothing-disentangled 3D human models progressively."
"Extensive experiments demonstrate that TELA effectively disentangles the human body and each cloth."

Quotes

"To solve this, we propose a layer-wise clothed human representation, where the human body and each clothing are represented with separate neural radiance fields (NeRFs)."
"We propose a novel transparency-based stratified compositional rendering method to prevent the penetration between adjacent components (e.g., the human body and upper-body clothes)."
"We propose novel dual SDS losses to simultaneously supervise the rendered clothed human image and clothes-only image, which introduces more regularization on the cloth model and encourages it to decouple from the human body."

Key Insights Distilled From

TELA: Text to Layer-wise 3D Clothed Human Generation

by Junting Dong... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16748.pdf

Deeper Inquiries

How can the proposed layer-wise representation and progressive generation be extended to handle more complex clothing types, such as loose garments or accessories?

The proposed layer-wise representation and progressive generation framework can be extended to handle more complex clothing types by incorporating additional neural radiance fields (NeRFs) for different components of the clothing. For loose garments like dresses or skirts, separate NeRFs can be trained to capture the unique characteristics and dynamics of these clothing items. Each layer-wise representation can focus on specific attributes of the garment, such as the flow, texture, and shape, allowing for a more detailed and accurate generation process.
To handle accessories, such as hats, bags, or jewelry, additional NeRFs can be introduced to represent these items separately from the main clothing components. By incorporating specific NeRFs for accessories, the framework can generate detailed and realistic accessories that complement the overall outfit. The progressive generation strategy can then be adapted to sequentially generate each accessory, ensuring that they are seamlessly integrated with the rest of the outfit.
Furthermore, the transparency-based stratified compositional rendering method can be optimized to handle the complexities of layering different types of clothing and accessories. By refining the rendering process to account for the interactions between multiple layers of clothing and accessories, the framework can accurately capture the intricate details and interactions between different garment components.

What are the potential limitations of the current NeRF-based representation, and how could alternative 3D representations, such as textured meshes, be integrated to further improve the quality and efficiency of the generated models?

While NeRF-based representations offer high-quality results for 3D human and clothing generation, they come with certain limitations. One limitation is the computational complexity and time-consuming optimization process required for training NeRF models, which can hinder real-time applications and interactive experiences. Additionally, NeRFs may struggle with capturing fine details and intricate textures, especially in complex clothing designs or accessories.
To address these limitations, alternative 3D representations, such as textured meshes, can be integrated into the framework to enhance the quality and efficiency of the generated models. Textured meshes provide a more efficient way to represent detailed surface textures and patterns, making them suitable for capturing intricate clothing designs and accessories. By incorporating textured meshes alongside NeRFs, the framework can leverage the strengths of both representations to achieve higher fidelity and realism in the generated models.
Furthermore, textured meshes can improve the efficiency of the generation process by reducing the computational overhead associated with NeRF optimization. By utilizing textured meshes for certain components of the clothing or accessories that require detailed textures, the framework can optimize the generation pipeline for faster and more interactive applications without compromising on quality.

Given the disentangled human and clothing models, how could the framework be adapted to enable interactive clothing composition and virtual try-on applications in real-time?

To enable interactive clothing composition and virtual try-on applications in real-time using the disentangled human and clothing models, the framework can be adapted in the following ways:

Real-time Rendering: Implement a real-time rendering engine that can quickly generate and display the disentangled human and clothing models as the user interacts with the application. This requires optimizing the rendering process to ensure smooth and responsive performance.

User Interaction: Incorporate intuitive user interfaces that allow users to interactively select, mix, and match different clothing items and accessories on the 3D human model. This can involve drag-and-drop functionalities, color selection tools, and resizing options for a seamless virtual try-on experience.

Dynamic Simulation: Integrate physics-based simulations to enable realistic cloth dynamics and interactions. This can enhance the virtual try-on experience by simulating how the clothing moves and drapes on the 3D human model as the user adjusts different parameters.

Feedback Mechanisms: Implement feedback mechanisms that allow users to provide input on the fit, style, and overall look of the virtual outfits. This feedback can be used to further refine the clothing composition and enhance the user experience.

By incorporating these adaptations, the framework can transform into an interactive platform for virtual clothing composition and try-on, providing users with a realistic and engaging experience in real-time.