Sketch2Human: A Deep Generative Framework for Controllable Full-Body Human Image Synthesis with Disentangled Geometry and Appearance

Core Concepts

Sketch2Human is a novel deep generative framework that can synthesize realistic full-body human images by allowing users to control the geometry through a semantic sketch and the appearance through a reference image.

Abstract

The key highlights and insights of this work are:

Sketch2Human is the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control).

The system consists of two main modules: Sketch Image Inversion and Body Generator Tuning.

The Sketch Image Inversion module trains a sketch encoder to invert the input semantic sketch to a latent code in the StyleGAN-Human latent space. This encoder is trained directly supervised by sketches rather than real images to achieve accurate geometry inversion.

The Body Generator Tuning module fine-tunes the pretrained StyleGAN-Human with appearance-transferred and geometry-preserved training data synthesized via style mixing. This allows the generator to achieve disentangled geometry and appearance control.

Extensive experiments demonstrate that Sketch2Human outperforms related techniques in terms of flexible and disentangled control of geometry and appearance, as well as visual quality.

The method can handle hand-drawn sketches as well as synthetic sketches, showing its robustness to different sketch styles.

Stats

"Geometry- and appearance-controlled full-body human image generation is an interesting but challenging task."
"Existing solutions are either unconditional or dependent on coarse conditions (e.g., pose, text), thus lacking explicit geometry and appearance control of body and garment."
"Sketching offers such editing ability and has been adopted in various sketch-based face generation and editing solutions."

Quotes

"Directly adapting sketch-based face generation to full-body generation often fails to produce high-fidelity and diverse results due to the high complexity and diversity in the pose, body shape, and garment shape and texture."
"Recent geometrically controllable diffusion-based methods mainly rely on prompts to generate appearance and it is hard to balance the realism and the faithfulness of their results to the sketch when the input is coarse."

Key Insights Distilled From

Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control

by Linzi Qu,Jia... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15889.pdf

Deeper Inquiries

How could the proposed Sketch2Human framework be extended to handle 3D human modeling and animation

The proposed Sketch2Human framework could be extended to handle 3D human modeling and animation by incorporating additional layers of complexity and information. One way to achieve this is by integrating 3D modeling techniques into the framework. This could involve using volumetric representations of human bodies in the latent space, allowing for the generation of 3D human models from semantic sketches and reference images. By incorporating 3D geometry information, the framework could generate realistic 3D human models with detailed geometry and appearance control.
Furthermore, to enable animation, the framework could be enhanced with rigging and skeletal animation capabilities. By incorporating rigging information into the latent space and training the model to understand skeletal structures, the framework could generate animated sequences of human movements based on semantic sketches and appearance references. This would allow for the creation of dynamic and lifelike 3D human animations with controllable geometry and appearance.

What are the potential limitations of the current approach, and how could they be addressed in future work

One potential limitation of the current approach is the reliance on synthetic data for training the model. While synthetic data can provide a large and diverse training set, it may not fully capture the complexity and variability of real-world human bodies and appearances. To address this limitation, future work could focus on incorporating more real-world data into the training process. This could involve collecting and annotating a larger dataset of real human images with corresponding semantic sketches and appearance references to improve the model's performance on real-world data.
Another limitation could be the challenge of handling extreme poses, body shapes, and garment textures. The current approach may struggle to accurately generate human images in cases where the input sketches or appearance references depict highly complex or unusual poses, body shapes, or garments. To address this, future work could explore advanced techniques for handling extreme variations in geometry and appearance, such as incorporating hierarchical modeling or attention mechanisms to focus on specific body parts or details.

How could the disentanglement of geometry and appearance be further improved to enable more fine-grained control over specific body parts and garment details

To improve the disentanglement of geometry and appearance for more fine-grained control over specific body parts and garment details, several approaches could be considered. One approach is to introduce additional latent codes or style vectors in the latent space to represent specific body parts or garment attributes. By disentangling the latent space further, the model could learn to control individual body parts or garment details independently, allowing for more precise editing and manipulation.
Additionally, incorporating attention mechanisms or spatial transformers into the model architecture could help the model focus on specific regions of the image during generation, enabling more targeted control over geometry and appearance. By attending to specific body parts or garment details, the model could enhance the fidelity and realism of the generated human images while maintaining fine-grained control over different aspects of the image.
Furthermore, exploring techniques from the field of image segmentation and object detection could help improve the disentanglement of geometry and appearance. By leveraging semantic segmentation information or object detection outputs, the model could better understand the spatial layout of different body parts and garment elements, leading to more accurate and detailed control over specific regions of the generated human images.