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Author

Yu-Ju Tsai

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#artificial intelligence Review Oct 2026

EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling

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...

Rui-Han Yu, Yu-Ju Tsai, Mu-Yao Niu et al. · 0 citations
Preprint Oct 2026

PROWBench: Do Video Models Render What the Program Specifies?

Programmable world models separate executable dynamics from visual generation, offering a promising foundation for next-generation game engines. However, their visual adherence to explicit rules and interactions remains insufficiently evaluated. Existing benchmarks assess visual quality, controllability, and instructio...

Zheng-Hui Huang, Gui-Xu Lin, Yu-Ju Tsai et al. · 0 citations
#artificial intelligence Preprint Sep 2026

KaiNinja: Extending Native 3D Generators to the Part Level

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.

Rui-Han Yu, Lian Fu, Mu-Yao Niu et al. · 0 citations

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