一段手机视频，就能生成你的4D数字人
你拍一段普通手机视频，AI就能生成一个你可以从任意角度观看、甚至放进游戏或VR里的3D动态人——这是4DAnyone做的事。过去这类技术要么需要多台相机同步拍摄，要么生成的视角一多就崩。研究者发现根本原因是AI的注意力机制有容量上限：当要生成的视角太多，它只能分批处理，结果前后批之间颜色、形状对不上。他们用两个技巧解决：一是把已生成的参考视角压缩成固定长度的混合分辨率信息，不随视角增多而膨胀；二是在去噪过程中让不同批次轮流交换信息，先保证整体结构一致，再细化细节。实验表明，生成的视频质量、以及后续重建的4D模型，都明显优于现有方法。它不是你明天就能用的App，但它是从专业动捕走向手机自拍的关键一步。
📄 原文摘要(英文)
We present 4DAnyone, a framework for reconstructing 4D humans from an uncalibrated monocular video by generating reconstruction-grade multiview-consistent videos and lifting them into 4D Gaussian Splatting (4DGS). Existing camera-controlled video diffusion models synthesize plausible novel-view videos but fail to maintain consistency when scaled to the tens of target views required for 4DGS reconstruction. We identify this failure as a bounded-attention-context problem: when target views exceed the capacity of a single DiT forward pass, they must be split into groups, exposing two coupled bottlenecks. On the reference-context side, conditioning on all previously generated views grows as O(N), weakening cross-view appearance guidance. On the target-context side, disjoint groups cannot directly exchange information, causing global structural drift. 4DAnyone addresses both bottlenecks with two complementary designs: Reference Context Packing (RCP) compresses growing reference views into a fixed-length mixed-resolution context with O(1) reference-context complexity, while Target Context Routing (TCR) rotates target-view groupings during denoising to share context across groups at high-noise steps and stabilize details at low-noise steps. We further build the MVGameHuman dataset using our in-house game engine and combine it with light-stage and in-the-wild video datasets for training. Experiments on DNA-Rendering and DyMVHumans show that 4DAnyone outperforms prior methods in both novel-view video quality and downstream 4DGS reconstruction, with robust in-the-wild generalization. See our project page for video results and source code: https://4danyone.github.io.