SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (2404.01225v2)

Abstract: Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper, we propose a new 4D motion modeling paradigm, SurMo, that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template, which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows. Our project page is at: https://taohuumd.github.io/projects/SurMo/

• The paper introduces a surface-based 4D motion model that jointly captures spatial and temporal dynamics for dynamic human rendering.
• It employs a surface-based triplane representation and physical motion decoding to efficiently synthesize high-fidelity novel views from sparse multi-view data.
• Empirical evaluations demonstrate SurMo's superiority over state-of-the-art methods in resolving fast motion and lighting-dependent shadowing effects.

SurMo: A New Paradigm for Dynamic Human Rendering Leveraging Surface-based 4D Motion Modeling

Introduction

In the paper titled "SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering," Tao Hu, Fangzhou Hong, and Ziwei Liu introduce a novel framework aimed at synthesizing dynamic human figures from sparse multi-view video data. By addressing the limitations present in existing methodologies, which primarily focus on static pose conditioning and lack an effective mechanism for capturing and leveraging temporal dynamics, this work proposes a structured approach to integrate both the spatial and temporal aspects of motion for enhanced human image synthesis.

Key Contributions

• Innovative Paradigm: The SurMo framework stands out by jointly modeling the temporal dynamics alongside the human appearance within a unified schema, utilizing a surface-based motion representation which is distinct from traditional volumetric or pose-guided representations.
• Surface-based Triplane Representation: At the core of SurMo lies the surface-based triplane representation, which efficiently encodes both spatial and temporal motion aspects on the dense surface manifold of a statistical body template. This compact formulation significantly contributes to the framework's ability to generalize novel view synthesis with sparse training observations.
• Physical Motion Decoding: The framework introduces a physical motion decoding strategy focused on encouraging learning of physical motion by predicting both spatial and temporal derivatives for the next time step during training. This approach advances the understanding and manipulation of temporal clothing offsets and secondary motion dynamics which are critical for realistic rendering.
• 4D Appearance Decoding and Optimization: Leveraging an efficacious volumetric surface-conditioned renderer coupled with a geometry-aware super-resolution mechanism, SurMo efficiently renders high-quality images conditioned on dynamic inputs. The optimization strategy integrates multiple losses, including adversarial, perceptual, and face identity losses, ensuring high fidelity in the final output.

Empirical Evaluation

SurMo's performance was rigorously evaluated against several state-of-the-art methods across three datasets with varying dynamics and complexities. The quantitative assessments demonstrate SurMo's superiority in handling novel-view synthesis, fast motion sequences, and motion-dependent shadowing effect, establishing new benchmarks in dynamic human rendering.

• Quantitative Analysis: Across all evaluated datasets and metrics, SurMo consistently outperformed existing approaches like Neural Body, HumanNeRF, Instant-NVR, among others, by notable margins. This highlights SurMo's effectiveness in synthesizing time-varying appearances with high fidelity.
• Qualitative Observations: Besides the numerical improvements, qualitative inspections reveal SurMo's adeptness at capturing and rendering fine-grained details such as clothing wrinkles and motion-dependent shadows under various lighting conditions and actions, aspects where other methods falter.

Future Directions and Implications

The SurMo framework introduces a paradigm shift in dynamic human rendering, emphasizing the critical role of surface-based motion modeling. Its capability to precisely capture and render dynamic human figures from sparse observations holds significant promise for applications across virtual reality, digital entertainment, and beyond.

Future work may explore further advancements in physical motion decoding techniques and the adaptation of the surface-based triplane representation to encompass a wider range of motion dynamics. Additionally, the potential for real-time rendering and the adaptation to varying textures and clothing types present exciting avenues for research and application development.

Conclusion

By effectively synthesizing dynamic human figures from limited observational data, SurMo represents a significant step forward in the field of human rendering. Its innovative use of a surface-based triplane model for motion representation, coupled with a holistic modeling of temporal dynamics, sets a new standard for realism and efficiency in the field.