SpatialEdit 500K SpatialEdit 500K is a synthetic training dataset for fine grained image spatial editing. It is built for learning geometry aware edits such as object moving, object rotation, and camera viewpoint change. The dataset was introduced in the paper SpatialEdit: Benchmarking Fine Grained Image Spatial Editing. It is generated with a controllable rendering pipeline to provide structured spatial transformations at scale. Project Resources GitHub Repository: EasonXiao 888/SpatialEdit Model: SpatialEdit 16B Benchmark: SpatialEdit Bench Highlights Large scale synthetic data: 500,000 samples for spatially grounded image editing. Comprehensive transformations: Covers both object centric (moving, rotation) and camera centric transformations. High fidelity: Generated with a controllable Blender pipeline rendering objects across diverse backgrounds with systematic camera trajectories. Precise labels: Provides precise ground truth transformations for spatial manipulation tasks. Citation
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