Surface-based 4D Motion Modeling for Dynamic Human Rendering

المفاهيم الأساسية

A new paradigm for learning dynamic humans from videos that jointly models temporal motions and human appearances in a unified framework, based on an efficient surface-based triplane representation that encodes both spatial and temporal motion relations.

الملخص

The paper proposes a new paradigm for learning dynamic humans from videos, which jointly models temporal motions and human appearances in a unified framework. The key contributions are:

Surface-based 4D motion encoding: The method extracts an expressive 4D motion representation from 3D body mesh sequences, which includes both static pose and temporal dynamics. This 4D motion input is then projected onto the dense 2D surface UV manifold of a clothless body template, and a motion encoder is employed to lift the 2D features into a 3D surface-based triplane representation that encodes both spatial and temporal motion relations.

Physical motion decoding: A motion decoder is introduced to enforce the learning of spatial and temporal motion relations by decoding the intermediate motion features to predict the spatial derivatives (surface normal) and temporal derivatives (surface velocity) at the next timestep.

4D appearance decoding: The surface-based motion triplane is rendered into high-quality images through a volumetric surface-conditioned renderer and an efficient geometry-aware super-resolution module.

Extensive experiments on multiple datasets validate the effectiveness of the proposed surface-based 4D motion representation in rendering high-fidelity time-varying appearances, especially for fast motions and motion-dependent shadows, outperforming state-of-the-art methods.

الإحصائيات

The paper uses 3D body mesh sequences obtained from training video as input.

اقتباسات

"At the core of the paradigm is a feature encoder-decoder framework with three key components: 1) surface-based motion encoding; 2) physical motion decoding; and 3) 4D appearance decoding."
"We achieve state-of-the-art results and show that our new paradigm is capable of learning high-fidelity appearances from fast motion sequences (e.g., AIST++ dance videos) or synthesizing motion-dependent shadows in challenging scenarios."

الرؤى الأساسية المستخلصة من

SurMo

by Tao Hu,Fangz... في arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01225.pdf

استفسارات أعمق

How can the proposed surface-based 4D motion representation be extended to other applications beyond human rendering, such as animating virtual characters or analyzing human motion patterns

The proposed surface-based 4D motion representation can be extended to other applications beyond human rendering by adapting the framework to animate virtual characters or analyze human motion patterns. For animating virtual characters, the surface-based triplane can be used to model the motion of different body parts or objects in the virtual environment. By capturing the spatial and temporal relations on the surface manifold, the framework can generate realistic and dynamic animations for virtual characters. Additionally, for analyzing human motion patterns, the surface-based triplane can be utilized to track and analyze movements in various scenarios such as sports analytics, physical therapy, or biomechanics research. By applying the motion modeling paradigm to different datasets and scenarios, valuable insights can be gained into human motion patterns and behaviors.

What are the potential limitations of the current approach, and how could it be further improved to handle more complex human motions, clothing, or environmental interactions

One potential limitation of the current approach is its effectiveness in handling extremely complex human motions, clothing interactions, or environmental dynamics. To further improve the framework in handling these challenges, several enhancements can be considered. Firstly, incorporating more advanced machine learning techniques such as reinforcement learning or generative adversarial networks can help in capturing intricate motion details and interactions. Secondly, integrating physics-based simulations or biomechanical models can enhance the realism of the rendered motions, especially for scenarios involving complex clothing dynamics or environmental interactions. Additionally, expanding the dataset diversity to include a wider range of motions, clothing types, and environmental conditions can improve the model's generalizability and robustness in handling complex scenarios.

The paper focuses on modeling temporal dynamics of human motion, but how could the framework be adapted to also capture the dynamics of other deformable objects, such as clothing or hair

To adapt the framework to capture the dynamics of other deformable objects like clothing or hair, modifications can be made to the surface-based triplane representation. By extending the triplane to include additional features specific to clothing or hair dynamics, such as fabric properties, elasticity, or wind interactions, the framework can effectively model the deformations and movements of these objects. Furthermore, incorporating specialized rendering techniques tailored for clothing or hair simulation, such as cloth simulation algorithms or hair physics models, can enhance the realism of the rendered results. By integrating these enhancements into the existing framework, the model can be adapted to accurately capture the dynamics of various deformable objects beyond human motion.