CapHuman: Capture Your Moments in Parallel Universes (2402.00627v3)

Abstract: We concentrate on a novel human-centric image synthesis task, that is, given only one reference facial photograph, it is expected to generate specific individual images with diverse head positions, poses, facial expressions, and illuminations in different contexts. To accomplish this goal, we argue that our generative model should be capable of the following favorable characteristics: (1) a strong visual and semantic understanding of our world and human society for basic object and human image generation. (2) generalizable identity preservation ability. (3) flexible and fine-grained head control. Recently, large pre-trained text-to-image diffusion models have shown remarkable results, serving as a powerful generative foundation. As a basis, we aim to unleash the above two capabilities of the pre-trained model. In this work, we present a new framework named CapHuman. We embrace the "encode then learn to align" paradigm, which enables generalizable identity preservation for new individuals without cumbersome tuning at inference. CapHuman encodes identity features and then learns to align them into the latent space. Moreover, we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner. Extensive qualitative and quantitative analyses demonstrate our CapHuman can produce well-identity-preserved, photo-realistic, and high-fidelity portraits with content-rich representations and various head renditions, superior to established baselines. Code and checkpoint will be released at https://github.com/VamosC/CapHuman.

• The paper introduces CapHuman, a framework that preserves individual identity while enabling precise control over head poses and expressions.
• It employs an 'encode then learn to align' approach using pre-trained models like FaceNet and CLIP to extract and align global and local features.
• Experiments demonstrate that CapHuman outperforms traditional methods with only a single reference image, delivering high-fidelity, photorealistic outputs.

Analysis of CapHuman: Capture Your Moments in Parallel Universes

The paper presents a novel methodology for human-centric image synthesis defined by its framework, CapHuman. This methodology challenges conventional image synthesis approaches by focusing on preserving individual identity while offering precise control over human features such as head positions, poses, facial expressions, and illuminations in varying contexts. This essay explores the technical intricacies of the CapHuman framework, evaluates its performance against state-of-the-art methodologies, and speculates on its potential implications in computer vision and artificial intelligence.

CapHuman is constructed atop a pre-trained text-to-image diffusion model, specifically Stable Diffusion, allowing it to leverage the model's foundational capabilities. The framework diverges from other personalization techniques by endorsing an "encode then learn to align" paradigm. This paradigm facilitates generalizable identity preservation without necessitating additional tuning at inference. Identity features are divided into global and local characteristics, extracted using pre-trained models like FaceNet for global recognition and CLIP's image encoder for local, detailed features. These features are then aligned with the latent feature space of CapHuman using cross-attention mechanisms, ensuring that individual identity is preserved throughout the image generation process.

The introduction of 3D Morphable Models (3DMM), like FLAME, permits flexible head control, tapping into an established model known for capturing a wide range of facial and head variances. By using 3D-aware representations, CapHuman ensures geometric consistency across synthesized images, a notable improvement over existing techniques which often lack such precision. Through this integration, CapHuman provides substantial improvements in synthesizing high-fidelity images that accurately manipulate head poses and facial expressions while maintaining identity integrity.

Experimentally, CapHuman outperformed established baselines in identity preservation and head control, even when using only a single reference image. It demonstrates significant efficiency and quality improvements over methods like Textual Inversion, DreamBooth, and others which require cumbersome test-time fine-tuning. Notably, CapHuman excels in generating photorealistic images that reflect a rich diversity of contexts and expressions as demonstrated in qualitative assessments.

The implications of this research are extensive, providing potential enhancements in areas requiring personalized image synthesis. The capacity to generate accurate and photorealistic human images has significant utility in gaming, virtual reality, and digital identity management. Furthermore, as the framework can adapt to various stylistic demands, it offers creative flexibility in media production and entertainment industries. It also opens pathways for adaptive AI systems capable of interacting with users through customized avatars that maintain a consistent identity over various expressions and settings.

Future developments might focus on expanding image synthesis beyond head and facial features to incorporate full-body manipulation, thereby addressing more comprehensive human-centric requirements. Additionally, the integration of dynamic real-time controls could enhance interactive applications, supporting scenarios where immediate visual response to user interaction is necessary.

In summation, CapHuman represents an advancement in the field of image synthesis by merging deep identity preservation with acute control over visual aspects, thereby broadening the practical and theoretical horizons in artificial human representation. Its adaptability and precision ensure that it holds promise for ongoing research and development in artificial intelligence and computer vision, paving the way for more intelligent and responsive visual synthesis technologies.