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Semantics-aware Motion Retargeting with Vision-Language Models for Preserving Motion Characteristics


Core Concepts
The core message of this work is to leverage the extensive knowledge of vision-language models to extract and maintain meaningful motion semantics during the motion retargeting process, thereby producing high-quality retargeted motions that accurately preserve the original motion's characteristics.
Abstract
This paper presents a novel semantics-aware motion retargeting method that integrates the capabilities of vision-language models to extract semantic embeddings and facilitate the preservation of motion semantics. The proposed approach involves a two-stage pipeline: Skeleton-aware Pre-training: The retargeting network, consisting of a graph motion encoder and decoder, is initially trained at the skeletal level to establish a robust initialization for motion retargeting. The training objective includes reconstruction loss, cycle consistency loss, adversarial loss, and joint relationship loss. Semantics & Geometry Fine-tuning: The pre-trained network is further refined and fine-tuned for each source-target character pair to preserve motion semantics and satisfy geometry constraints. The semantics consistency loss aligns the latent semantic embeddings extracted from a frozen vision-language model (BLIP-2) for both the source and target motions. The geometry constraint is satisfied by minimizing the interpenetration loss between the limb vertices and the body mesh. The experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in generating high-quality retargeted motions while accurately preserving motion semantics. The authors also conduct extensive ablation studies to validate the importance of the two-stage training pipeline and the effectiveness of the vision-language model in extracting motion semantics.
Stats
The mean square error (MSE) between retargeted joint positions and ground truth is 0.284. The local MSE is 0.229. The interpenetration percentage is 3.50%. The image-text matching (ITM) score is 0.680, indicating high semantics preservation. The Fréchet inception distance (FID) of motion semantics is 0.436, showing the retargeted motion closely matches the source motion semantics. The semantics consistency loss is 0.143, demonstrating the effectiveness of the proposed semantics-aware approach.
Quotes
"The core message of this work is to leverage the extensive knowledge of vision-language models to extract and maintain meaningful motion semantics during the motion retargeting process, thereby producing high-quality retargeted motions that accurately preserve the original motion's characteristics." "To establish a connection between the vision-language model and motion semantics extraction, we employ the differentiable skinning and rendering modules to translate 3D motions into image sequences. Subsequently, we adopt visual question answering with guiding questions to inquire about the most relevant motion semantics from the vision-language model." "To guarantee the preservation of motion semantics during motion retargeting, we introduce a semantics consistency loss that enforces the semantic embeddings of the retargeted motion to closely align with those of the source motion."

Key Insights Distilled From

by Haodong Zhan... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2312.01964.pdf
Semantics-aware Motion Retargeting with Vision-Language Models

Deeper Inquiries

How can the proposed method be extended to handle more complex motion types, such as interactions between multiple characters or full-body motions?

The proposed semantics-aware motion retargeting method can be extended to handle more complex motion types by incorporating additional components and strategies: Multi-character Interactions: To handle interactions between multiple characters, the model can be modified to consider the spatial relationships and interactions between the characters. This can involve developing a mechanism to detect and analyze the interactions between different characters in the scene, such as collisions, handovers, or coordinated movements. Full-body Motions: For full-body motions, the model can be enhanced to capture the dynamics and coordination of movements across the entire body. This may involve incorporating a more comprehensive skeletal representation that includes detailed joint movements and interactions between different body parts. Hierarchical Motion Representation: Introducing a hierarchical motion representation that captures both individual character motions and their interactions can help in handling complex scenarios. This can involve encoding the motions of each character separately and then integrating them into a unified representation that accounts for the interactions. Dynamic Semantic Extraction: Enhancing the vision-language model to dynamically extract and interpret motion semantics in real-time can enable the model to adapt to changing and complex motion scenarios. This can involve continuous feedback loops to refine the semantic understanding of the motions as they unfold. Adaptive Fine-tuning: Implementing adaptive fine-tuning mechanisms that adjust the model parameters based on the complexity and diversity of the motion types can improve the model's performance in handling more intricate scenarios. This can involve reinforcement learning techniques to optimize the model for different motion types. By incorporating these extensions and strategies, the semantics-aware motion retargeting framework can be enhanced to effectively handle more complex motion types, including interactions between multiple characters and full-body motions.

How can the potential limitations of the vision-language model in extracting motion semantics be addressed, and how can future research overcome these limitations?

The vision-language model used for extracting motion semantics may have limitations that can impact the accuracy and effectiveness of the semantics-aware motion retargeting framework. To address these limitations and improve the model's performance, the following strategies can be considered: Dataset Augmentation: Increasing the diversity and size of the training dataset used for pre-training the vision-language model can help in capturing a wider range of motion semantics and improving the model's generalization capabilities. Fine-tuning with Motion-specific Data: Fine-tuning the vision-language model with motion-specific data, such as annotated motion sequences with detailed semantics, can enhance the model's understanding of motion-related concepts and improve its performance in extracting motion semantics. Incorporating Motion Context: Integrating contextual information about the motion, such as the spatial relationships between body parts, temporal dynamics, and motion patterns, can help the model better interpret and extract meaningful semantics from the motion data. Model Architecture Optimization: Optimizing the architecture of the vision-language model to specifically focus on motion semantics extraction, such as incorporating attention mechanisms that prioritize motion-related features, can improve the model's ability to extract relevant semantics. Continuous Learning and Adaptation: Implementing mechanisms for continuous learning and adaptation, where the model can update its semantic understanding based on new data and feedback, can help overcome limitations and improve the model's performance over time. By addressing these potential limitations and incorporating these strategies, future research can enhance the vision-language model's capabilities in extracting motion semantics and contribute to the advancement of semantics-aware motion retargeting frameworks.

How can the semantics-aware motion retargeting framework be integrated with other computer graphics applications, such as virtual reality or game development, to enhance the realism and immersion of animated characters?

The integration of the semantics-aware motion retargeting framework with other computer graphics applications, such as virtual reality (VR) or game development, can significantly enhance the realism and immersion of animated characters. Here are some ways in which this integration can be achieved: Real-time Motion Retargeting: Implementing the semantics-aware motion retargeting framework in real-time systems for VR or games can enable dynamic and adaptive motion retargeting of characters based on user interactions and environmental conditions, enhancing the realism of character animations. Interactive Character Animation: Integrating the framework with interactive character animation tools in VR environments or games can allow users to manipulate and retarget character motions in real-time, providing a more immersive and engaging experience. Enhanced Character Customization: Leveraging the framework to retarget motions for customizable characters in VR simulations or games can offer users a high degree of personalization and realism in character animations, enhancing the overall user experience. Natural Motion Synthesis: Using the semantics-aware motion retargeting framework to synthesize natural and lifelike motions for characters in VR or games can create more realistic and believable animations, contributing to a more immersive virtual environment. Cross-platform Compatibility: Ensuring the compatibility of the framework with different platforms and devices used in VR and game development can facilitate seamless integration and deployment, allowing for consistent and high-quality motion retargeting across various applications. By integrating the semantics-aware motion retargeting framework with VR and game development applications, developers can create more realistic, interactive, and immersive experiences for users, enhancing the overall quality and engagement of animated characters in virtual environments.
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