# GeoNeXt：将视频生成模型用作几何学习器

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-28 08:00
- AIHOT 分数：33
- AIHOT 链接：https://aihot.virxact.com/items/cmtgmdkaf0300rochpygib9xc
- 原文链接：https://arxiv.org/abs/2608.28549

## AI 摘要

GeoNeXt将预训练视频生成模型改造为统一的几何估计框架，把深度与表面法线估计重构为下一帧预测任务，无需分别训练任务专属模型或大规模微调图像扩散骨干。该方法在零样本单目深度和表面法线估计上超越此前任务专属及统一生成式方案，且训练数据需求大幅降低，性能可与基于超100倍数据训练的有判别力SOTA方法比肩。

## 正文

Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveraging off-the-shelf image diffusion models, they either (i) train task-specific geometry models (for depth and surface normal estimation) independently, losing the opportunity of exploring the intrinsic correlation of these geometric targets, or (ii) jointly fine-tune modified image diffusion backbones (e.g., altered self-attention), which typically demands substantial labeled data. To overcome these limitations in a principled fashion, we repurpose pretrained video generative models as a unified and data-efficient framework for geometry estimation, formulated innovatively as a next-frames prediction task. Our method, GeoNeXt, inherits naturally structured knowledge and richer priors from the video model, while further adapting them for joint modeling of images and geometry targets (image <-> geometry), enabling more data efficient and effective learning of geometry. Extensive experiments validate our method for zero-shot monocular depth and surface normal estimation across diverse datasets, outperforming both previous task-specific and unified generative competitors while using substantially less training data. Notably, our method rivals discriminative state-of-the-art approaches trained on over 100x more data and even standouts on several benchmarks.