Multimodal Large Language Model for 3D Human Pose Estimation and Reasoning

Konsep Inti

ChatPose is a multimodal Large Language Model (LLM) that can directly generate 3D human poses represented as SMPL parameters from text or image inputs, and reason about human poses using its general world knowledge.

Abstrak

The paper introduces ChatPose, a multimodal Large Language Model (LLM) that can understand and reason about 3D human poses. The key contributions are:

ChatPose embeds SMPL poses as distinct signal tokens within the LLM, enabling it to directly generate 3D body poses from both textual and visual inputs. This allows the LLM to leverage its powerful capabilities for tasks beyond traditional 3D pose estimation and generation.

The paper introduces two novel tasks that require reasoning about human poses: Speculative Pose Generation (SPG) and Reasoning-based Pose Estimation (RPE). These tasks go beyond classical pose estimation and generation by requiring the model to apply its general world knowledge to infer and reason about human poses.

Experiments show that ChatPose outperforms existing multimodal LLMs and task-specific methods on the newly proposed SPG and RPE tasks, demonstrating its ability to understand and reason about 3D human poses.

The paper first provides an overview of related work in 3D human pose estimation, language and pose, and multimodal LLMs. It then details the architecture and training of ChatPose, which integrates SMPL pose as a distinct modality within the LLM.
The key innovation is that by embedding SMPL poses within the LLM, ChatPose can leverage the LLM's general world knowledge to reason about human poses in complex ways. This enables the two new tasks of Speculative Pose Generation and Reasoning-based Pose Estimation, where the model must infer and generate 3D poses based on high-level textual descriptions or scene context, rather than explicit pose instructions.
Experiments show that ChatPose outperforms existing multimodal LLMs and task-specific methods on these new reasoning-focused tasks, while also performing competitively on classical 3D pose estimation and generation. This demonstrates the power of integrating 3D human pose understanding within a general-purpose multimodal LLM.

Statistik

The SMPL pose of this person is .
The SMPL pose of the person is .
The SMPL format of this person's pose is .
The SMPL pose of the person wearing a green shirt is .

Kutipan

"The SMPL pose is ."
"Sure, it is ."
"The SMPL pose of the person is ."

Wawasan Utama Disaring Dari

ChatPose: Chatting about 3D Human Pose

by Yao Feng,Jin... pada arxiv.org 04-25-2024

https://arxiv.org/pdf/2311.18836.pdf

Pertanyaan yang Lebih Dalam

How could ChatPose's understanding of 3D human pose be extended to reason about full-body motion and dynamics?

To extend ChatPose's understanding of 3D human pose to reason about full-body motion and dynamics, the model could incorporate additional data and training focused on capturing the dynamics of movement. This could involve analyzing sequences of poses over time to understand how different poses transition into one another and how the body moves in a coordinated manner. By training on motion capture data or videos of human movement, ChatPose could learn to predict and generate realistic full-body motions based on initial poses and movement cues. Additionally, incorporating biomechanical principles and knowledge of human anatomy could help ChatPose simulate more natural and physically accurate movements.

How might ChatPose's pose reasoning capabilities be applied to tasks like human-robot interaction or virtual character animation?

ChatPose's pose reasoning capabilities could be applied to tasks like human-robot interaction or virtual character animation by enabling more natural and intuitive interactions between humans and robots or enhancing the realism of animated characters. For human-robot interaction, ChatPose could help robots interpret human body language and gestures, allowing for more effective communication and collaboration. By understanding human poses and movements, robots could respond appropriately to gestures, expressions, and postures, improving the overall interaction experience.
In virtual character animation, ChatPose could be used to generate lifelike animations by predicting and animating character movements based on textual or visual inputs. This could streamline the animation process by automatically generating poses and movements for characters in response to script descriptions or scene requirements. ChatPose's ability to reason about poses could also enhance the expressiveness and realism of virtual characters, making them more engaging and believable in virtual environments.

What other modalities beyond 3D pose could ChatPose integrate to further enhance its reasoning abilities about the physical world?

Beyond 3D pose, ChatPose could integrate additional modalities such as depth information, surface normals, and object interactions to enhance its reasoning abilities about the physical world. By incorporating depth data, ChatPose could better understand spatial relationships and distances between objects and individuals, improving its perception of the environment. Surface normals could provide information about object orientations and shapes, enabling ChatPose to reason about object interactions and physical constraints in a scene.
Furthermore, integrating tactile or haptic feedback data could enhance ChatPose's understanding of physical interactions and textures, allowing it to simulate realistic touch sensations and object manipulation. By combining multiple modalities, ChatPose could create a more comprehensive and nuanced understanding of the physical world, enabling it to reason about complex scenarios involving human-object interactions, spatial awareness, and physical dynamics.