# 4DAnyone：从随意单目视频重建4D人体

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-20 08:00
- AIHOT 分数：50
- AIHOT 链接：https://aihot.virxact.com/items/cmt2daxf802eiroc9aygpco99
- 原文链接：https://arxiv.org/abs/2608.20335

## AI 摘要

4DAnyone提出从无标定单目视频重建4D人体的框架，通过生成重建级多视角一致视频并提升至4D高斯泼溅（4DGS）。针对现有模型在数十个目标视角下的一致性失效问题，该方法以参考上下文打包（RCP）和目标任务上下文路由（TCR）分别实现O(1)参考复杂度与跨组上下文共享。在DNA-Rendering和DyMVHumans上，4DAnyone在视角视频质量与下游4DGS重建上均优于先前方法。

## 正文

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.