FashionEngine: An Interactive System for Generating and Editing 3D Clothed Humans

Grunnleggende konsepter

FashionEngine automates the 3D human production by leveraging a pre-trained 3D human diffusion model, a multimodality-UV space that aligns user inputs with the implicit UV latent space, and multimodality-UV aligned samplers for controllable generation and editing of 3D clothed humans.

Sammendrag

FashionEngine is an interactive system that enables the generation and editing of high-quality 3D clothed humans. It consists of three key components:

A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, providing strong priors for diverse generation and editing tasks.

A multimodality-UV space that encodes the texture appearance, shape topology, and textual semantics of human clothing in a canonical UV-aligned space. This aligns user inputs like texts, images, and sketches with the implicit UV latent space for controllable 3D human editing.

Multimodality-UV aligned samplers that learn to sample high-quality and diverse 3D humans from the diffusion prior for multimodal user inputs, enabling text-, sketch-, and image-driven generation and editing.

The system allows users to generate 3D clothed humans either randomly or conditionally from text descriptions or hand-drawing sketches. Users can then edit the generated humans interactively using text, reference images, or sketches. The final 3D humans can be adjusted in pose and shape before rendering into images or videos.
Extensive experiments validate FashionEngine's state-of-the-art performance for conditional generation and editing tasks. The interactive user interface enables both conditional and unconditional generation, as well as various editing tasks in a unified framework.

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FashionEngine

by Tao Hu,Fangz... klokken arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01655.pdf

Dypere Spørsmål

How can FashionEngine's multimodal editing capabilities be extended to handle more complex user inputs, such as natural language instructions or multi-part sketches?

FashionEngine's multimodal editing capabilities can be extended to handle more complex user inputs by incorporating advanced natural language processing (NLP) techniques for text inputs. This could involve training the system to understand a wider range of natural language instructions, including nuanced descriptions of clothing styles, colors, textures, and accessories. By leveraging pre-trained language models like BERT or GPT-3, FashionEngine can improve its ability to interpret and act upon detailed textual descriptions provided by users.
For multi-part sketches, FashionEngine can implement more sophisticated image processing algorithms to analyze and interpret different components of the sketch. This could involve segmenting the sketch into distinct regions representing various parts of the clothing or body, allowing for more precise editing based on different sections of the sketch. By enhancing the system's ability to understand and manipulate multi-part sketches, FashionEngine can offer users greater flexibility and control in editing 3D human avatars.

How could the integration of physics-based simulation or motion capture data further enhance the realism and animation capabilities of the generated 3D humans?

Integrating physics-based simulation or motion capture data into FashionEngine can significantly enhance the realism and animation capabilities of the generated 3D humans. By incorporating physics simulations, the system can ensure that the clothing and body movements of the avatars behave realistically in different scenarios. This means that the clothing will drape and move naturally as the avatar changes poses or interacts with the environment, adding a layer of authenticity to the generated 3D humans.
Motion capture data can be used to capture real human movements and apply them to the generated avatars, resulting in more lifelike animations. By mapping motion capture data onto the 3D avatars, FashionEngine can create fluid and natural movements that closely mimic real human actions. This integration can be particularly useful for applications in virtual try-on scenarios, where users can see how clothing moves and fits on a virtual representation of themselves in a realistic manner.

What are the potential challenges in scaling FashionEngine to handle a wider diversity of 3D human appearances, including different genders, ages, and body types?

Scaling FashionEngine to handle a wider diversity of 3D human appearances, including different genders, ages, and body types, presents several challenges:

Data Diversity: One challenge is ensuring that the training data used to develop FashionEngine is diverse and representative of the full spectrum of human appearances. Collecting and annotating a large and varied dataset that includes different genders, ages, body types, and styles can be resource-intensive and time-consuming.

Model Generalization: Ensuring that the models used in FashionEngine can generalize well to unseen variations in human appearances is crucial. The system must be able to adapt to new inputs and generate accurate representations of diverse individuals without overfitting to specific characteristics present in the training data.

Bias and Fairness: There is a risk of bias in the generated avatars if the training data is not balanced across different demographics. Ensuring fairness and inclusivity in the generated 3D human appearances requires careful consideration of representation and diversity in the dataset and model training.

Computational Resources: Handling a wider diversity of 3D human appearances may require more computational resources for training and inference. Scaling FashionEngine to accommodate a larger variety of inputs while maintaining real-time performance can be a significant technical challenge.

Addressing these challenges will be essential for FashionEngine to effectively handle a broader range of 3D human appearances and provide users with inclusive and realistic avatar generation capabilities.