Tinker：扩散模型给3D的礼物——无需逐场景优化，从稀疏输入实现多视图一致编辑
Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
AI总结：
该研究提出Tinker框架，无需逐场景优化即可从1-2张图像实现高保真3D编辑，利用预训练扩散模型的潜在3D感知能力保证多视图一致性，还构建了首个大规模多视图编辑数据集，显著降低3D内容创建门槛。
摘要：
我们提出Tinker，一个通用的高保真3D编辑框架，可在单样本和少样本模式下运行，无需任何逐场景微调。与需要大量逐场景优化以确保多视图一致性或生成数十个一致编辑输入视图的现有技术不同，Tinker仅从1或2张图像就能生成鲁棒、多视图一致的编辑结果。这种能力源于重新利用预训练扩散模型，从而释放其潜在的3D感知能力。为推动该领域研究，我们构建了首个大规模多视图编辑数据集和数据管道，涵盖多样场景和风格。基于此数据集，我们开发的框架无需逐场景训练即可生成多视图一致的编辑视图，包含两个新组件：（1）参考多视图编辑器：实现跨所有视角连贯的精确参考驱动编辑；（2）任意视图转视频合成器：利用视频扩散的时空先验，即使从稀疏输入也能完成高质量场景补全和新视图生成。通过大量实验，Tinker显著降低了通用3D内容创建的门槛，在编辑、新视图合成和渲染增强任务上达到最先进性能。我们认为Tinker是迈向真正可扩展零样本3D编辑的关键一步。项目网页：https://aim-uofa.github.io/Tinker
英文摘要：
We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edited input views, Tinker delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D awareness. To drive research in this space, we curate the first large-scale multi-view editing dataset and data pipeline, spanning diverse scenes and styles. Building on this dataset, we develop our framework capable of generating multi-view consistent edited views without per-scene training, which consists of two novel components: (1) Referring multi-view editor: Enables precise, reference-driven edits that remain coherent across all viewpoints. (2) Any-view-to-video synthesizer: Leverages spatial-temporal priors from video diffusion to perform high-quality scene completion and novel-view generation even from sparse inputs. Through extensive experiments, Tinker significantly reduces the barrier to generalizable 3D content creation, achieving state-of-the-art performance on editing, novel-view synthesis, and rendering enhancement tasks. We believe that Tinker represents a key step towards truly scalable, zero-shot 3D editing. Project webpage: https://aim-uofa.github.io/Tinker
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
