Generative Model for Realistic Clothed and Textured 3D Human Meshes with Pose-Dependent Deformations

Core Concepts

SCULPT is a novel 3D generative model that learns to represent the geometry and appearance distribution of clothed human bodies, enabling the generation of realistic textured 3D human meshes with pose-dependent deformations.

Abstract

The paper presents SCULPT, a 3D generative model that can produce realistic clothed and textured human meshes. The key contributions are:

SCULPT learns the geometry and appearance distribution of clothed human bodies by leveraging medium-scale 3D scan datasets and large-scale 2D image datasets in an unpaired learning procedure.

The geometry model is trained on the CAPE dataset to learn pose-dependent vertex displacements from the SMPL body model. The texture model is then trained in an unsupervised way on 2D fashion images, conditioned on the intermediate features of the geometry model to ensure coherence between appearance and geometry.

SCULPT provides explicit control over clothing type and color by conditioning both the geometry and texture generators on attribute labels automatically extracted from the 2D images using language models.

The generated textured meshes are compatible with existing 3D rendering engines, enabling seamless integration into various applications.

Extensive experiments demonstrate SCULPT's superior performance compared to state-of-the-art 3D generative models for clothed human bodies, in terms of geometry quality, texture quality, and controllability.

Stats

"We compute 63069 displacement maps from these registered meshes."
"We collected 16362 fashion images, normalized the images (human centered in the middle) and removed the background."

Quotes

"SCULPT is a generative model that takes a geometry code zg ∼N(0,I512×512), a texture code zt ∼N(0,I512×512), body pose θ ∈R69, clothing geometry type cg ∈{0,1}6, and clothing texture description ct∈R512 as input, and it generates a clothed 3D body mesh M := {V,C} with a texture image Itex ∈R256×256×3."

Key Insights Distilled From

SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes

by Soubhik Sany... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2308.10638.pdf

Deeper Inquiries

How could SCULPT's performance be further improved by incorporating additional data modalities, such as video sequences or multi-view 3D scans

Incorporating additional data modalities like video sequences or multi-view 3D scans could significantly enhance SCULPT's performance in several ways. Firstly, video sequences would provide temporal information, allowing the model to capture dynamic clothing movements and deformations over time. This would enable SCULPT to generate more realistic and natural-looking clothing simulations, especially for activities like walking, running, or dancing. By training on video data, SCULPT could learn the dynamics of clothing behavior and improve its accuracy in capturing realistic clothing interactions with body movements.
Secondly, multi-view 3D scans would offer a more comprehensive understanding of the clothing geometry from different perspectives. By training on multi-view data, SCULPT could learn to generate clothing that looks accurate and consistent from various angles, improving the overall visual quality and realism of the generated meshes. This would also help in addressing the limitations of the current approach in handling complex clothing structures and loose-fitting garments by providing a more detailed and comprehensive representation of the clothing geometry.
Additionally, incorporating video sequences and multi-view 3D scans would enable SCULPT to learn spatial and temporal correlations between clothing appearance and body movements, leading to more coherent and realistic clothing simulations. By leveraging these additional data modalities, SCULPT could achieve a higher level of fidelity and accuracy in generating clothed human meshes with detailed and dynamic clothing behaviors.

What are the potential limitations of the current approach in modeling highly complex or loose-fitting clothing, and how could the model be extended to handle a broader range of clothing types

The current approach of SCULPT may face limitations in modeling highly complex or loose-fitting clothing due to the inherent constraints of the SMPL body model and the explicit mesh representation. To address these limitations and handle a broader range of clothing types, several extensions could be considered:

Adaptive Mesh Topologies: Introducing adaptive mesh topologies that can dynamically adjust based on the complexity and structure of the clothing could enhance SCULPT's capability to model diverse clothing types. By incorporating topological variations in the mesh representation, SCULPT could better capture intricate details and loose-fitting garments like skirts or dresses.

Clothing Layering: Implementing a layering system that allows for stacking multiple clothing items on top of each other could enable SCULPT to simulate complex outfits with different layers and textures. This would involve developing a mechanism to handle interactions between layered clothing items and ensure realistic rendering of overlapping garments.

Physics-Based Simulation: Integrating physics-based simulation techniques into the model could improve the realism of clothing dynamics and interactions. By simulating cloth physics, SCULPT could generate more natural-looking folds, wrinkles, and movements in the clothing, especially for loose-fitting garments that require realistic draping and deformation.

Hybrid Approaches: Combining the strengths of neural networks with traditional cloth simulation methods could offer a comprehensive solution for handling complex clothing structures. By integrating physics-based cloth simulation with neural network-based texture and geometry generation, SCULPT could achieve a balance between realism and efficiency in modeling diverse clothing types.

By incorporating these extensions and enhancements, SCULPT could overcome the limitations in modeling highly complex or loose-fitting clothing, expanding its capabilities to generate a broader range of clothing styles with increased fidelity and accuracy.

Given the advancements in language models, how could SCULPT leverage more sophisticated text-based control over the generated clothing, beyond just color and type, to enable even finer-grained control over the appearance

To leverage more sophisticated text-based control over the generated clothing beyond color and type, SCULPT could explore the following strategies:

Texture Patterns: Introduce text-based descriptions of texture patterns such as stripes, polka dots, floral prints, etc., to enable finer control over the appearance of the clothing. By incorporating a broader range of texture descriptors, SCULPT could generate clothing with diverse and intricate patterns, enhancing the realism and variety of the generated meshes.

Material Properties: Incorporate text-based cues related to material properties like silk, denim, leather, etc., to influence the visual and tactile qualities of the clothing. By considering material descriptors in the text input, SCULPT could simulate different fabric textures and finishes, adding richness and authenticity to the generated clothing.

Clothing Details: Include text-based instructions for specific clothing details such as buttons, zippers, pockets, ruffles, etc., to customize the design and style of the garments. By allowing users to specify detailed features through text input, SCULPT could create clothing with intricate embellishments and design elements, enhancing the overall visual appeal and realism of the generated meshes.

Adaptive Styling: Implement a text-based styling system that interprets fashion preferences, trends, or historical references to generate clothing designs tailored to specific themes or aesthetics. By analyzing textual cues related to fashion styles and trends, SCULPT could adapt its generation process to produce clothing that aligns with the desired fashion concepts, offering personalized and on-trend clothing simulations.

By incorporating these advanced text-based controls, SCULPT could empower users to create highly customized and detailed clothing designs, expanding the creative possibilities and versatility of the model in generating diverse and realistic clothed human meshes.