insight - Artificial Intelligence - # Image Generation

HyperHuman: Generating Hyper-Realistic Human Images with Latent Structural Diffusion

Q: How can the incorporation of deep priors like LLMs enhance text-to-pose generation

Incorporating deep priors like Latent Linear Models (LLMs) can significantly enhance text-to-pose generation by providing a structured and informative prior distribution for the pose estimation process. LLMs are capable of capturing complex dependencies and correlations within the data, allowing for more accurate and detailed pose predictions based on textual inputs. By leveraging the learned representations from LLMs, the model can better understand the underlying structure of human poses, leading to improved accuracy in generating realistic and controllable human images.

Q: What are the limitations of existing pose/depth/normal estimators for in-the-wild humans

Existing pose/depth/normal estimators for in-the-wild humans have certain limitations that can impact their performance in generating subtle details like fingers and eyes accurately. These limitations include: Limited Training Data: Estimators may not have been trained on diverse datasets with a wide range of poses, lighting conditions, and backgrounds commonly found in real-world scenarios. Complexity of Human Anatomy: The intricate nature of human anatomy, especially when considering fine details like fingers or facial features, can challenge estimators to capture all nuances accurately. Noise Sensitivity: Estimators may be sensitive to noise or artifacts present in input images, leading to inaccuracies or distortions in estimating poses or structural information. Generalization Issues: Estimators trained on specific datasets may struggle to generalize well to unseen data distributions encountered during inference.

Q: How can noise schedules be optimized to improve the learning of monotonous structural maps

Optimizing noise schedules is crucial for improving the learning of monotonous structural maps by diffusion models: Zero-Terminal SNR Control: Enforcing a zero-terminal Signal-to-Noise Ratio (SNR) helps eliminate low-frequency information leakage common in monotonous structural maps. Consistent Noise Levels Across Modalities: Ensuring consistent noise levels across different modalities being denoised allows for better feature fusion among branches handling various targets. Dense Sampling Strategy: Employing dense sampling with similar time-steps across different targets facilitates better learning without increasing sparsity issues associated with perturbation sampling methods. Adaptive Noise Scaling: Implementing adaptive noise scaling techniques based on target characteristics can help address challenges posed by varying structures such as depth maps versus surface-normal maps. By optimizing these aspects of noise schedules within diffusion models, it becomes possible to enhance the joint learning capabilities required for synthesizing high-quality images with coherent structure under diverse scenarios effectively.

Core Concepts

Generating hyper-realistic human images through a unified framework, HyperHuman, utilizing latent structural diffusion for high-quality and diverse results.

Abstract

The article introduces HyperHuman, a framework for generating hyper-realistic human images by capturing correlations between appearance and structure. It proposes a Latent Structural Diffusion Model to denoise RGB, depth, and surface-normal simultaneously. The Structure-Guided Refiner refines conditions for detailed generation. Extensive experiments show state-of-the-art performance in diverse scenarios.

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Stats

HumanVerse dataset consists of 340M images with annotations.
Extensive experiments demonstrate superior performance.
FID: 17.18, KID×1k: 4.11

Quotes

"To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities."
"Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network."
"Extensive experiments demonstrate that our framework yields the state-of-the-art performance."

Key Insights Distilled From

HyperHuman

by Xian Liu,Jia... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2310.08579.pdf

Deeper Inquiries

How can the incorporation of deep priors like LLMs enhance text-to-pose generation

Incorporating deep priors like Latent Linear Models (LLMs) can significantly enhance text-to-pose generation by providing a structured and informative prior distribution for the pose estimation process. LLMs are capable of capturing complex dependencies and correlations within the data, allowing for more accurate and detailed pose predictions based on textual inputs. By leveraging the learned representations from LLMs, the model can better understand the underlying structure of human poses, leading to improved accuracy in generating realistic and controllable human images.

What are the limitations of existing pose/depth/normal estimators for in-the-wild humans

Existing pose/depth/normal estimators for in-the-wild humans have certain limitations that can impact their performance in generating subtle details like fingers and eyes accurately. These limitations include:

Limited Training Data: Estimators may not have been trained on diverse datasets with a wide range of poses, lighting conditions, and backgrounds commonly found in real-world scenarios.
Complexity of Human Anatomy: The intricate nature of human anatomy, especially when considering fine details like fingers or facial features, can challenge estimators to capture all nuances accurately.
Noise Sensitivity: Estimators may be sensitive to noise or artifacts present in input images, leading to inaccuracies or distortions in estimating poses or structural information.
Generalization Issues: Estimators trained on specific datasets may struggle to generalize well to unseen data distributions encountered during inference.

How can noise schedules be optimized to improve the learning of monotonous structural maps

Optimizing noise schedules is crucial for improving the learning of monotonous structural maps by diffusion models:

Zero-Terminal SNR Control: Enforcing a zero-terminal Signal-to-Noise Ratio (SNR) helps eliminate low-frequency information leakage common in monotonous structural maps.
Consistent Noise Levels Across Modalities: Ensuring consistent noise levels across different modalities being denoised allows for better feature fusion among branches handling various targets.
Dense Sampling Strategy: Employing dense sampling with similar time-steps across different targets facilitates better learning without increasing sparsity issues associated with perturbation sampling methods.
Adaptive Noise Scaling: Implementing adaptive noise scaling techniques based on target characteristics can help address challenges posed by varying structures such as depth maps versus surface-normal maps.

By optimizing these aspects of noise schedules within diffusion models, it becomes possible to enhance the joint learning capabilities required for synthesizing high-quality images with coherent structure under diverse scenarios effectively.