HumanGenesis: Agent-Based Geometric and Generative Modeling for Synthetic Human Dynamics

• URL: http://arxiv.org/abs/2508.09858v1
• Date: Wed, 13 Aug 2025 14:50:19 GMT
• Title: HumanGenesis: Agent-Based Geometric and Generative Modeling for Synthetic Human Dynamics
• Authors: Weiqi Li, Zehao Zhang, Liang Lin, Guangrun Wang,
• Abstract summary: We present textbfHumanGenesis, a framework that integrates geometric and generative modeling through four collaborative agents.<n>HumanGenesis achieves state-of-the-art performance on tasks including text-guided synthesis, video reenactment, and novel-pose generalization.
• Score: 60.737929335600015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: \textbf{Synthetic human dynamics} aims to generate photorealistic videos of human subjects performing expressive, intention-driven motions. However, current approaches face two core challenges: (1) \emph{geometric inconsistency} and \emph{coarse reconstruction}, due to limited 3D modeling and detail preservation; and (2) \emph{motion generalization limitations} and \emph{scene inharmonization}, stemming from weak generative capabilities. To address these, we present \textbf{HumanGenesis}, a framework that integrates geometric and generative modeling through four collaborative agents: (1) \textbf{Reconstructor} builds 3D-consistent human-scene representations from monocular video using 3D Gaussian Splatting and deformation decomposition. (2) \textbf{Critique Agent} enhances reconstruction fidelity by identifying and refining poor regions via multi-round MLLM-based reflection. (3) \textbf{Pose Guider} enables motion generalization by generating expressive pose sequences using time-aware parametric encoders. (4) \textbf{Video Harmonizer} synthesizes photorealistic, coherent video via a hybrid rendering pipeline with diffusion, refining the Reconstructor through a Back-to-4D feedback loop. HumanGenesis achieves state-of-the-art performance on tasks including text-guided synthesis, video reenactment, and novel-pose generalization, significantly improving expressiveness, geometric fidelity, and scene integration.

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