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3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first benchmark for long-horizon, part-level 3D editing, with natural...
The Programmable World Model is introduced, a framework that decouples world-state evolution from visual observation generation and demonstrates the effectiveness of separating explicit state evolution from generative rendering for building persistent, programmable worlds.
KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation, is the first 3D generative model trained on agent-authored part data and finds that whole-object fidelity improves over the same backbone fine-tuned on the same dataset.
This work shows that complex scenes with hundreds of objects can instead be generated compositionally by adapting a strong single-object 3D generative prior to multi-view observations, and introduces UE-MeshyScene, a photorealistic benchmark of densely cluttered scenes with hundreds of objects, per-object annotations,...
This work introduces Surprise Forcing, a training-free framework that treats both limitations as online resource-allocation problems and improves long-horizon consistency and visual quality while retaining real-time streaming throughput.
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