洞察 - Software Development - # Text-Driven Garment Generation

Generating Customized Garments from Text Guidance: DressCode's Autoregressive Sewing and PBR Texture Creation

Q: How can the dataset be expanded to include more complex garment types and stitching relationships to improve the versatility of the generated garments?

Expanding the dataset to include more complex garment types and stitching relationships is crucial for enhancing the versatility of the generated garments. Here are some strategies to achieve this: Diverse Garment Types: Include a wider range of garment types such as evening gowns, tailored suits, sportswear, and traditional attire from various cultures. This diversity will enable the model to learn and generate a broader spectrum of designs. Multi-Layered Garments: Incorporate garments with multiple layers like jackets with inner linings, hoodies with pockets, or dresses with intricate overlays. This will challenge the model to understand and generate complex stitching relationships. Unconventional Designs: Introduce unconventional designs like asymmetrical dresses, garments with unique cutouts, or avant-garde silhouettes. This will push the model to think creatively and expand its design capabilities. Detailed Stitching Information: Provide detailed stitching information for each garment type, including seam types, stitching styles, and embellishments. This will help the model create more realistic and intricate sewing patterns. User-Generated Content: Allow users to contribute their garment designs to the dataset. This crowdsourcing approach can bring in a wide variety of styles and inspirations, enriching the dataset with unique and innovative designs. By incorporating these elements into the dataset, the model will be exposed to a more diverse and comprehensive set of examples, enabling it to generate a wider range of complex and realistic garments with intricate stitching relationships.

Q: How can the integration of multi-modal inputs, such as both text and images, further enhance the effectiveness and quality of the garment generation process?

Integrating multi-modal inputs, such as both text and images, can significantly enhance the effectiveness and quality of the garment generation process in the following ways: Improved Context Understanding: Combining text descriptions with visual images provides a more comprehensive understanding of the desired garment. The model can leverage both textual details and visual cues to generate more accurate and contextually relevant designs. Enhanced Creativity: By incorporating images along with text prompts, users can convey their design ideas more vividly and creatively. Visual references can inspire the model to generate garments that closely align with the user's vision. Fine-Tuned Details: Images can provide specific details that may be challenging to describe accurately in text, such as intricate patterns, fabric textures, or color gradients. This additional visual information can help the model refine its output with more precision. Validation and Feedback: Users can validate the generated garments against the visual references provided, ensuring that the output aligns with their expectations. This feedback loop can improve the model's accuracy and user satisfaction. Cross-Modal Learning: Training the model on multi-modal inputs can facilitate cross-modal learning, where the model learns to associate text descriptions with corresponding visual features. This can enhance the model's ability to generate realistic and visually appealing garments. By integrating text and images as input modalities, the garment generation process can benefit from a richer and more nuanced understanding of design requirements, leading to higher-quality outputs and improved user experiences.

Q: What are the potential ethical concerns regarding the use of text-driven generation models, such as bias and copyright issues, and how can they be addressed?

The use of text-driven generation models raises several ethical concerns, including bias and copyright issues. Here's how these concerns can be addressed: Bias Mitigation: To address bias in text-driven generation models, it is essential to diversify the training data to represent a wide range of cultural backgrounds, styles, and preferences. Implementing bias detection algorithms and conducting regular audits can help identify and mitigate biases in the model's outputs. Fairness and Inclusivity: Ensure that the training data is inclusive and representative of diverse demographics to prevent the model from perpetuating stereotypes or favoring certain groups. Incorporating fairness metrics and conducting bias impact assessments can help promote fairness and inclusivity in the generated outputs. Copyright Compliance: To address copyright issues, models should be trained on legally obtained and properly licensed datasets. Implementing content filtering mechanisms to avoid generating copyrighted material and providing clear guidelines on intellectual property rights can help mitigate copyright infringement risks. Transparency and Accountability: Models should be transparent about their data sources, training processes, and potential biases. Providing users with information on how the model operates and enabling them to understand and interpret the generated outputs can enhance accountability and trust. User Consent and Control: Users should have control over the data they provide and the generated outputs. Implementing mechanisms for user consent, data deletion, and output customization can empower users to make informed decisions and protect their privacy rights. By proactively addressing these ethical concerns through responsible data practices, transparency, and user empowerment, text-driven generation models can uphold ethical standards and promote trustworthiness in their applications.

核心概念

DressCode, a novel text-driven framework, generates high-quality, CG-friendly garments with detailed sewing patterns and physically-based rendering (PBR) textures, enabling user-friendly interaction, pattern completion, and texture editing.

摘要

The paper introduces DressCode, a framework that enables the generation of customized garments from text prompts. The key components are:

SewingGPT: A GPT-based autoregressive model that generates sewing patterns from text guidance. It quantizes sewing patterns into token sequences and uses a decoder-only Transformer with text-conditioned embeddings to generate the patterns autoregressively.
PBR Texture Generation: The framework tailors a pre-trained Stable Diffusion model to generate tile-based, physically-based rendering (PBR) textures (diffuse, normal, and roughness maps) from text prompts.
User-friendly Interaction: DressCode integrates the sewing pattern and texture generation capabilities to allow users to create customized garments through natural language interaction. It also supports pattern completion and texture editing.

The framework is evaluated through qualitative and quantitative comparisons with state-of-the-art 3D generation methods, demonstrating its ability to generate high-quality, CG-friendly garments that closely align with input text prompts. A comprehensive user study further validates the practical utility and potential of DressCode in production settings.

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统计

Our method achieves the highest CLIP score (0.327) compared to other text-to-3D generation methods (Wonder3D*: 0.302, RichDreamer: 0.324).
Our method takes around 3 minutes to generate garments, while Wonder3D* takes around 4 minutes and RichDreamer takes around 4 hours.
Our method supports PBR texture generation, texture editing, and draping on human models, which the other methods do not.

引用

"DressCode, a novel text-driven framework, generates high-quality, CG-friendly garments with detailed sewing patterns and physically-based rendering (PBR) textures, enabling user-friendly interaction, pattern completion, and texture editing."
"Benefiting from the autoregressive model, our method can complete the entire sewing pattern by utilizing probabilistic predictions provided by the model upon receiving partial pattern information."
"Our method, yielding more accurate results, demonstrates robust generation capabilities with text prompts."

从中提取的关键见解

DressCode: Autoregressively Sewing and Generating Garments from Text Guidance

by Kai He,Kaixi... 在 arxiv.org 04-30-2024

https://arxiv.org/pdf/2401.16465.pdf

DressCode: Autoregressively Sewing and Generating Garments from Text Guidance

更深入的查询

How can the dataset be expanded to include more complex garment types and stitching relationships to improve the versatility of the generated garments?

Expanding the dataset to include more complex garment types and stitching relationships is crucial for enhancing the versatility of the generated garments. Here are some strategies to achieve this:

Diverse Garment Types: Include a wider range of garment types such as evening gowns, tailored suits, sportswear, and traditional attire from various cultures. This diversity will enable the model to learn and generate a broader spectrum of designs.

Multi-Layered Garments: Incorporate garments with multiple layers like jackets with inner linings, hoodies with pockets, or dresses with intricate overlays. This will challenge the model to understand and generate complex stitching relationships.

Unconventional Designs: Introduce unconventional designs like asymmetrical dresses, garments with unique cutouts, or avant-garde silhouettes. This will push the model to think creatively and expand its design capabilities.

Detailed Stitching Information: Provide detailed stitching information for each garment type, including seam types, stitching styles, and embellishments. This will help the model create more realistic and intricate sewing patterns.

User-Generated Content: Allow users to contribute their garment designs to the dataset. This crowdsourcing approach can bring in a wide variety of styles and inspirations, enriching the dataset with unique and innovative designs.

By incorporating these elements into the dataset, the model will be exposed to a more diverse and comprehensive set of examples, enabling it to generate a wider range of complex and realistic garments with intricate stitching relationships.

How can the integration of multi-modal inputs, such as both text and images, further enhance the effectiveness and quality of the garment generation process?

Integrating multi-modal inputs, such as both text and images, can significantly enhance the effectiveness and quality of the garment generation process in the following ways:

Improved Context Understanding: Combining text descriptions with visual images provides a more comprehensive understanding of the desired garment. The model can leverage both textual details and visual cues to generate more accurate and contextually relevant designs.

Enhanced Creativity: By incorporating images along with text prompts, users can convey their design ideas more vividly and creatively. Visual references can inspire the model to generate garments that closely align with the user's vision.

Fine-Tuned Details: Images can provide specific details that may be challenging to describe accurately in text, such as intricate patterns, fabric textures, or color gradients. This additional visual information can help the model refine its output with more precision.

Validation and Feedback: Users can validate the generated garments against the visual references provided, ensuring that the output aligns with their expectations. This feedback loop can improve the model's accuracy and user satisfaction.

Cross-Modal Learning: Training the model on multi-modal inputs can facilitate cross-modal learning, where the model learns to associate text descriptions with corresponding visual features. This can enhance the model's ability to generate realistic and visually appealing garments.

By integrating text and images as input modalities, the garment generation process can benefit from a richer and more nuanced understanding of design requirements, leading to higher-quality outputs and improved user experiences.

What are the potential ethical concerns regarding the use of text-driven generation models, such as bias and copyright issues, and how can they be addressed?

The use of text-driven generation models raises several ethical concerns, including bias and copyright issues. Here's how these concerns can be addressed:

Bias Mitigation: To address bias in text-driven generation models, it is essential to diversify the training data to represent a wide range of cultural backgrounds, styles, and preferences. Implementing bias detection algorithms and conducting regular audits can help identify and mitigate biases in the model's outputs.

Fairness and Inclusivity: Ensure that the training data is inclusive and representative of diverse demographics to prevent the model from perpetuating stereotypes or favoring certain groups. Incorporating fairness metrics and conducting bias impact assessments can help promote fairness and inclusivity in the generated outputs.

Copyright Compliance: To address copyright issues, models should be trained on legally obtained and properly licensed datasets. Implementing content filtering mechanisms to avoid generating copyrighted material and providing clear guidelines on intellectual property rights can help mitigate copyright infringement risks.

Transparency and Accountability: Models should be transparent about their data sources, training processes, and potential biases. Providing users with information on how the model operates and enabling them to understand and interpret the generated outputs can enhance accountability and trust.

User Consent and Control: Users should have control over the data they provide and the generated outputs. Implementing mechanisms for user consent, data deletion, and output customization can empower users to make informed decisions and protect their privacy rights.

By proactively addressing these ethical concerns through responsible data practices, transparency, and user empowerment, text-driven generation models can uphold ethical standards and promote trustworthiness in their applications.