SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing
Image spatial editing performs geometry-driven transformations, allowing precise control over object layout and camera viewpoints. Current models are insufficient for fine-grained spatial manipulations, motivating a dedicated assessment suite. Our contributions are listed: (i) We introduce SpatialEdit-Bench, a complete benchmark that evaluates spatial editing by jointly measuring perceptual plausibility and geometric fidelity via viewpoint reconstruction and framing analysis. (ii) To address the data bottleneck for scalable training, we construct SpatialEdit-500k, a synthetic dataset generated with a controllable Blender pipeline that renders objects across diverse backgrounds and systematic camera trajectories, providing precise ground-truth transformations for both object- and camera-centric operations. (iii) Building on this data, we develop SpatialEdit-16B, a baseline model for fine-grained spatial editing. Our method achieves competitive performance on general editing while substantially outperforming prior methods on spatial manipulation tasks. All resources will be made public at https://github.com/EasonXiao-888/SpatialEdit.
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Yicheng Xiao
claimed_verified
2026-04-13T19:24:50.421Z
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YC Xiao
EasonXiao-888
user
EasonXiao-888
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Wenhu Zhang
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Lin Song
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Yukang Chen
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Wenbo Li
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Nan Jiang
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Tianhe Ren
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Haokun Lin
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Wei Huang
claimed_verified
2026-04-07T08:34:38.578Z
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Wei Huang
AaronHuangWei
user
AaronHuangWei
69d46d012c523d36431fb0b1
Haoyang Huang
69d46d012c523d36431fb0b2
Xiu Li
69d46d012c523d36431fb0b3
Nan Duan
69d46d012c523d36431fb0b4
Xiaojuan Qi
SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing
Image spatial editing performs geometry-driven transformations, allowing precise control over object layout and camera viewpoints. Current models are insufficient for fine-grained spatial manipulations, motivating a dedicated assessment suite. Our contributions are listed: (i) We introduce SpatialEdit-Bench, a complete benchmark that evaluates spatial editing by jointly measuring perceptual plausibility and geometric fidelity via viewpoint reconstruction and framing analysis. (ii) To address the data bottleneck for scalable training, we construct SpatialEdit-500k, a synthetic dataset generated with a controllable Blender pipeline that renders objects across diverse backgrounds and systematic camera trajectories, providing precise ground-truth transformations for both object- and camera-centric operations. (iii) Building on this data, we develop SpatialEdit-16B, a baseline model for fine-grained spatial editing. Our method achieves competitive performance on general editing while substantially outperforming prior methods on spatial manipulation tasks. All resources will be made public at https://github.com/EasonXiao-888/SpatialEdit.
A new benchmark and dataset are introduced for evaluating fine-grained spatial editing capabilities, along with a model that demonstrates superior performance on spatial manipulation tasks.
SpatialEdit-Bench SpatialEdit-500k SpatialEdit-16B spatial editing geometric fidelity viewpoint reconstruction framing analysis synthetic dataset Blender pipeline camera trajectories object-centric operations camera-centric operations