From Text to Pose to Image: Improving Diffusion Model Control and Quality (2411.12872v2)

Abstract: In the last two years, text-to-image diffusion models have become extremely popular. As their quality and usage increase, a major concern has been the need for better output control. In addition to prompt engineering, one effective method to improve the controllability of diffusion models has been to condition them on additional modalities such as image style, depth map, or keypoints. This forms the basis of ControlNets or Adapters. When attempting to apply these methods to control human poses in outputs of text-to-image diffusion models, two main challenges have arisen. The first challenge is generating poses following a wide range of semantic text descriptions, for which previous methods involved searching for a pose within a dataset of (caption, pose) pairs. The second challenge is conditioning image generation on a specified pose while keeping both high aesthetic and high pose fidelity. In this article, we fix these two main issues by introducing a text-to-pose (T2P) generative model alongside a new sampling algorithm, and a new pose adapter that incorporates more pose keypoints for higher pose fidelity. Together, these two new state-of-the-art models enable, for the first time, a generative text-to-pose-to-image framework for higher pose control in diffusion models. We release all models and the code used for the experiments at https://github.com/clement-bonnet/text-to-pose.

• The paper introduces the first text-to-pose model using a novel contrastive pretraining method (CLaPP) and transformer architecture, achieving a 78% win-rate over dataset-based methods.
• It enhances image generation with an improved pose adapter that captures detailed facial and hand keypoints, outperforming existing adapters on aesthetic metrics.
• The framework bridges semantic text prompts with precise human poses, refining diffusion model control and setting the stage for scalable, ethically guided advancements.

From Text to Pose to Image: Enhancing Diffusion Model Control

The research paper "From Text to Pose to Image: Improving Diffusion Model Control and Quality" investigates the advancement of text-to-image diffusion models by introducing a novel text-to-pose-to-image framework. The objective is to improve the controllability of diffusion models, particularly in relation to human pose fidelity, while also maintaining high aesthetics in the generated images.

Challenges in Current Models

The authors address two primary challenges in current diffusion models. The first challenge is generating poses from a variety of semantic text descriptions. Traditionally, this involves matching poses from datasets based on image captions or using pose-estimation models and transfer techniques like GANs. The second challenge is ensuring that generated images conditioned on specific poses maintain high fidelity and aesthetic quality, an issue prevalent in existing state-of-the-art systems such as the SDXL-Tencent adapter, which neglects fine details like faces and hands.

Proposed Framework and Methods

The paper introduces a two-fold solution to these challenges:

1. Text-to-Pose (T2P) Model: At the core of this innovation is the development of the first text-to-pose generative model. Utilizing a novel text-pose contrastive metric named CLaPP (Contrastive Language-Pose Pretraining), this model aligns poses with text descriptions. The T2P model is built on a transformer architecture optimized to predict ordered sequences of keypoints associated with human body parts. It leverages Gaussian Mixture Models (GMM) to manage the inherent multi-modality of pose prediction. The T2P model's efficacy is validated through its competitive performance against nearest-neighbor search methods in pose selection tasks.
2. Pose Adapter: To enhance pose-specific image generation, the authors developed a state-of-the-art pose adapter. This component extends the ResNet-like structure of prior adapters by incorporating additional keypoints, significantly facial and hand details, thus improving pose fidelity. Training focused on high-quality images to align with the aesthetic demands of diffusion models.

Numerical and Comparative Analysis

A rigorous evaluation of the proposed models is presented. The T2P model demonstrates a 78% win-rate over dataset-based pose selection methods, validating its capacity to generalize and align with diverse text inputs. Similarly, the newly introduced pose adapter shows improved aesthetic scoring, surpassing the SDXL-Tencent adapter 70% of the time on aesthetic metrics and 76% of the time on human-preference scores using the COCO-Pose benchmark dataset.

Implications and Future Directions

The integration of these models into a text-to-pose-to-image framework signals a significant step forward in the field of conditional image generation. This approach introduces a layer of semantic control between text prompts and final image outputs, facilitating finer adjustments in pose while maintaining visual quality.

The research draws attention to broader ethical and practical considerations. Improved control over generated images raises questions about the authenticity and potential misuse of AI-generated content. As model capabilities progress, ethical guidelines and frameworks will be essential to prevent misleading or harmful applications.

While the framework addresses several existing limitations, future work could explore the scalability of the T2P model to broader and more diverse datasets, potentially enhancing its adaptability and precision. Additionally, integrating more sophisticated uncertainty management techniques could further refine inference in auto-regressive generative processes.

This paper not only contributes to the technical development of diffusion models but also poses important questions about the boundaries of synthetic image generation, offering a platform for further exploration in both AI capabilities and ethics.