GeoNeXt用视频模型做几何估计，数据效率高，性能还超过100倍数据训练的模型。
GeoNeXt方法将预训练的视频生成模型重新用于几何估计任务，将其创新地表述为下一帧预测任务。该方法在多个数据集上实现了零样本单目深度和表面法线估计，性能优于特定任务和统一生成式竞争对手，且使用显著更少的训练数据。GeoNeXt在多个基准测试中甚至能媲美使用超过100倍训练数据的判别式最先进方法。
Video Generative Models as Geometry Learner
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.