Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation (2412.13111v1)

Abstract: Text-driven human motion synthesis is capturing significant attention for its ability to effortlessly generate intricate movements from abstract text cues, showcasing its potential for revolutionizing motion design not only in film narratives but also in virtual reality experiences and computer game development. Existing methods often rely on 3D motion capture data, which require special setups resulting in higher costs for data acquisition, ultimately limiting the diversity and scope of human motion. In contrast, 2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities. In this paper, we explore leveraging 2D human motion extracted from videos as an alternative data source to improve text-driven 3D motion generation. Our approach introduces a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data. We first train a single-view 2D local motion generator on a large dataset of text-motion pairs. To enhance this model to synthesize 3D motion, we fine-tune the generator with 3D data, transforming it into a multi-view generator that predicts view-consistent local joint motion and root dynamics. Experiments on the HumanML3D dataset and novel text prompts demonstrate that our method efficiently utilizes 2D data, supporting realistic 3D human motion generation and broadening the range of motion types it supports. Our code will be made publicly available at https://zju3dv.github.io/Motion-2-to-3/.

An Overview of "Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation"

The paper, "Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation," presents a novel framework aimed at enhancing the generation of text-driven 3D human motions by effectively utilizing 2D motion data extracted from videos. This approach addresses several limitations inherent in current methodologies that often rely on costly and restricted 3D motion capture systems, which contribute to limited dataset diversity and scalability. Instead, the proposed framework capitalizes on the abundance and accessibility of 2D human motion data available through videos, which span diverse styles and activities.

Framework and Methodology

The authors introduce a comprehensive framework that strategically disentangles local joint motion from global movements. This structural division facilitates the efficient learning of local motion priors from the substantial 2D datasets. The process consists of training a single-view 2D local motion generator on extensive text-motion pair datasets. Following this, the generator is fine-tuned with 3D data to evolve into a multi-view generator capable of predicting view-consistent local joint motion and root dynamics.

2D Local Motion Generation: Initially, the model focuses on capturing local joint motion by abstracting 2D data from videos, thereby excluding global and camera-induced motion influences. This data is processed through a transformer-based diffusion model trained on a vast array of text-annotated human motion videos. This model, named 2D Motion Diffusion model, serves to establish a strong foundation of local motion priors without being subjected to the constraints of the global 3D motion dynamics.

Multi-view 3D Motion Generation: The foundational 2D model is then enhanced using 3D data. This step involves projecting the learned 2D sequences into a multi-view format. The model, termed Multi-view Diffusion model, integrates a view attention mechanism, ensuring consistency across different perspectives and accurately captures root velocity components necessary for global motion synthesis. This multi-view setup not only ensures coherent perspectives but also supports extended motion styles reflective of natural dynamics.

Results and Evaluation

The framework's efficacy is rigorously validated with quantitative experiments conducted on the HumanML3D dataset and evaluated against novel text prompts. Statistically, the system demonstrates efficient utilization of 2D data to produce realistic 3D human motion, which aligns well with given textual inputs. Compared to state-of-the-art methods limited to 3D training datasets, this approach exhibits notable improvements in generating a broad spectrum of motion types.

Implications and Future Directions

The implications of Motion-2-to-3 extend beyond significant cost reductions and dataset diversity enhancement. By successfully integrating 2D data into 3D motion synthesis, the framework paves the way for richer, more contextually accurate virtual experiences across multiple domains, including gaming, VR, and film production. It highlights a potential shift toward decentralized data approaches within the field of motion synthesis.

Future research can build upon this work by addressing noise and jitter in 2D data, exploring other neural architectures beyond diffusion models, and extending the framework to finer granularity in motion dynamics, such as hand movements or complex object interactions. Additionally, leveraging even larger datasets could further improve performance, particularly for generating motions from novel and diverse textual prompts.

In conclusion, the innovative use of 2D motion data as an augmentation tool for 3D motion generation demonstrates a resourceful and promising direction in synthesizing human motion from text, contributing significantly to the enhancement of computer-generated imagery and interactive digital environments.