arXiv:2408.15461v1 Announce Type: cross
Abstract: Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using only 1,000 training samples. The training of Hand1000 is divided into three stages with the first stage aiming to enhance the model’s understanding of hand anatomy by using a pre-trained hand gesture recognition model to extract gesture representation. The second stage further optimizes text embedding by incorporating the extracted hand gesture representation, to improve alignment between the textual descriptions and the generated hand images. The third stage utilizes the optimized embedding to fine-tune the Stable Diffusion model to generate realistic hand images. In addition, we construct the first publicly available dataset specifically designed for text-to-hand image generation. Based on the existing hand gesture recognition dataset, we adopt advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. Extensive experiments demonstrate that Hand1000 significantly outperforms existing models in producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing, and colors.
Analysis: Addressing Challenges in Text-to-Image Generation for Human Hands
Text-to-image generation models have shown remarkable advancements in recent years in generating realistic images from textual descriptions. However, these models often struggle when it comes to generating anatomically accurate representations of human hands. This article introduces a novel approach called Hand1000 that aims to address these challenges and enable the generation of realistic hand images with target gestures using only 1,000 training samples.
The complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands contribute to the issues faced by existing models. The proposed Hand1000 approach takes a multi-stage training process to tackle these challenges effectively.
Stage 1: Enhancing Understanding of Hand Anatomy
In the first stage, a pre-trained hand gesture recognition model is used to extract gesture representations. This step helps the model enhance its understanding of hand anatomy, which is crucial for generating accurate hand images. By leveraging the existing knowledge of gesture recognition, the model becomes more aware of the intricate details of hand movements and positioning.
Stage 2: Optimizing Text Embedding with Hand Gesture Representation
Building upon the extracted hand gesture representation, the second stage aims to optimize text embedding, improving alignment between textual descriptions and generated hand images. This stage ensures that the model incorporates the gesture information effectively, enabling it to generate hand images that align with the intended gestures described in the text. By considering detailed gesture information, the resulting hand images become more accurate and visually coherent.
Stage 3: Fine-tuning with Stable Diffusion Model
In the third stage, the optimized embedding produced in the previous stage is utilized to fine-tune the Stable Diffusion model. This model is responsible for generating realistic hand images. With the improved text embedding, the model can better translate textual descriptions into visually appealing hand images, considering factors such as hand morphology, shading, and texture. Fine-tuning allows the model to refine its understanding and generate high-quality images that faithfully represent the textual details.
In addition to the proposed approach, the article highlights the construction of the first publicly available dataset specifically designed for text-to-hand image generation. This dataset leverages advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. This dataset serves as a valuable resource for further research and development in the field.
Hand1000 demonstrates superior performance compared to existing models when it comes to producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing, and colors. By addressing the challenges of anatomically accurate hand representation, this approach contributes to the wider field of multimedia information systems and its various sub-domains, including animations, artificial reality, augmented reality, and virtual realities.