We present Flex4DHuman, a multi-view video diffusion model that transforms a monocular or sparse multi-view video of a dynamic subject into synchronized dense multi-view videos using only relative camera-pose conditioning. Unlike prior human-centric methods that rely on skeletons, depth maps, normals, or rendered target-view geometry, Flex4DHuman requires no explicit geometry priors and instead conditions generation through relative camera-pose positional encoding. The generated videos can be directly ingested by downstream reconstruction pipelines to create dynamic 4D Gaussian splats. Built on the Wan 2.1 1.3B text-to-video model, Flex4DHuman preserves the backbone architecture and encodes camera and view information through a five-axis positional encoding that extends spatio-temporal RoPE with view indices and continuous SE(3) relative camera geometry. A three-stage curriculum progressively trains the model for pose following, flexible reference-to-target view generation, and temporal rollout. To support temporal rollout, we train with clean historical target-view tokens. We also add multi-view captions to enable test-time text control. Combined with an off-the-shelf 4D Gaussian Splatting stage, our framework lifts monocular static-camera videos into dynamic 4D Gaussian splats. Experiments on DNA-Rendering and ActorsHQ show that Flex4DHuman surpasses prior state-of-the-art methods, while the same formulation generalizes to animal categories after mixed human-animal training. These capabilities make Flex4DHuman a practical step toward scalable 4D content creation from casual monocular videos for simulation, gaming, AR/VR, and video re-shooting.

翻译：摘要：本文提出Flex4DHuman——一种多视图视频扩散模型，仅通过相对相机位姿条件即可将动态对象的单目或稀疏多视图视频转换为同步的密集多视图视频。与以往依赖骨架、深度图、法向图或渲染目标视图几何的人体中心化方法不同，Flex4DHuman无需显式几何先验，而是通过相对相机位姿的位置编码来约束生成过程。生成的视频可直接输入下游重建管线，创建动态四维高斯泼溅场。该模型基于Wan 2.1 1.3B文本生成视频模型构建，保留骨干架构，通过五轴位置编码（在时空RoPE基础上扩展视图索引和连续SE(3)相对相机几何）编码相机与视图信息。采用三阶段渐进式训练策略，依次实现位姿跟随、灵活的参考视图到目标视图生成以及时序展开。为支持时序展开，我们使用干净的历史目标视图令牌进行训练，并加入多视图字幕以实现测试时文本控制。结合现成的四维高斯泼溅阶段，本框架可将单目静态摄像机视频提升为动态四维高斯泼溅场。在DNA-Rendering和ActorsHQ数据集上的实验表明，Flex4DHuman超越了现有最先进方法，且经混合人类-动物训练后，同一框架可泛化至动物类别。这些能力使Flex4DHuman成为从日常单目视频迈向可扩展四维内容创建（适用于仿真、游戏、增强现实/虚拟现实及视频重拍）的实用步骤。