For all the Posers
Unipose can understand, change and morph your pose as a MLLM
Paper: UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing
Researchers from CAS are interested in UniPose, a multimodal framework that uses Large Language Models (LLMs) for human pose comprehension, generation, and editing.
Hmm..What’s the background?
Current research in human pose understanding and generation primarily focuses on single tasks in isolation. In reality, we understand and communicate poses using various modalities, like 3D models, text, and images. Existing Multimodal Large Language Models (MLLMs) are also limited in their ability to comprehensively analyze human poses, especially regarding fine-grained details.
UniPose aims to overcome these limitations and unify pose comprehension, generation, and editing within a single framework.
So what is proposed in the research paper?
The research paper incorporates several key insights:
Pose Tokenizer: Converts 3D poses into discrete tokens using a Vector Quantized Variational Autoencoder (VQ-VAE). This allows seamless integration of pose data with text within the LLM
Visual Processor: UniPose uses a mixture of visual encoders, combining a CLIP visual encoder with a pose-specific encoder pre-trained on pose estimation tasks
Mixed Attention Mechanism: UniPose employs a mixed attention mechanism within the LLM to accommodate the non-sequential nature of pose tokens, which represent spatial joint positions
UniPose is the first framework to integrate seven core tasks related to pose comprehension, generation, and editing. UniPose achieves competitive performance across multiple tasks, demonstrating its effectiveness in pose comprehension, generation, and editing. UniPose exhibits zero-shot generalization capabilities, including text-enhanced pose estimation.
What’s next?
Future research will address the limitations of MLLM-based models in pose estimation, particularly the constraints of using a frozen visual encoder. The focus will be on developing techniques that allow LLMs to effectively integrate pose-relevant visual features from various encoders to improve their ability to handle complex pose estimation tasks.
Unipose can understand, change and morph your pose as a MLLM
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