研究人员开发了 RayDer，这是一种新颖的 transformer 模型，旨在改进来自真实世界视频的自监督新视角合成。该统一模型将相机估计、场景重建和渲染整合到一个单一骨干网络中，从而能够对动态视频内容进行稳定训练。RayDer 在数据和计算方面表现出可预测的幂律缩放，在各种基准测试中取得了具有竞争力的零样本性能。 AI
影响 通过利用通用视频数据，实现了更具可扩展性和鲁棒性的新视角合成，可能对 3D 重建和内容创作产生影响。
排序理由 该集群包含一篇详细介绍新模型及其性能的学术论文。
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arXiv:2605.31535v1 Announce Type: cross Abstract: Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network…
Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network system designs. We introduce RayDer, a unified, f…
RayDer is a unified feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering for self-supervised novel view synthesis, enabling stable training on real-world video through dynamic state absorption and demonstrating clean scaling behavior.