We present FloodDiffusion 2 (FD2), an efficient and controllable framework that builds upon FloodDiffusion (FD1), a state-of-the-art streaming motion generation model. While FD1 produces plausible motion, it suffers from low efficiency and limited controllability, as its attention design requires repeated computation o...
Yi-Yi Cai, Yu-Han Wu, Kun-Hang Li et al.· 0 citations
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
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
We present World2Motion, a framework that generates scene-aware 3D human motion and corresponding video from a single image and a text prompt. While existing 3D motion generators learn from motion datasets, their generalization is constrained by limited coverage of environments. In contrast, video world models such as...
Fang-Yuan Tu, Xiang-Yue Zhang, Yi-Yi Cai et al.· 0 citations
Triangular Resampling is introduced, a post-training method for mitigating long-horizon error accumulation in motion diffusion models that addresses the mismatch between ground-truth-derived training windows and model-generated inference states.
Kun-Hang Li, Yi-Yi Cai, Xiang-Yue Zhang et al.· 0 citations
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.
Zheng-Hui Huang, Gui-Xu Lin, Jia-Cheng Lin et al.· 6 citations· ⚡1
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
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,...
AlayaRenderer-Flash is introduced, a real-time-oriented generative forward world renderer that pushes AlayaRenderer from 0.56 FPS to 31.54 FPS, reaching the speed of play.
Gui-Xu Lin, Zheng-Hui Huang, Si-Qi Yang et al.· arXiv.org· 2 citations
The new version of AlayaWorld substantially revise how conditioning signals are represented and integrated into the model, replacing the previous depth-warping-based spatial memory with a streaming 3D point-cache renderer.
AlayaWorld Team Kaipeng Zhang, Chuan-Hao Li, Yi-Fan Zhan et al.· 5 citations· ⚡2
AlayaWorld enables open-ended real-time interaction, allowing users to freely navigate and perform diverse actions such as combat, spell casting, and monster summoning, and the framework unifies the complete development-from data preparation model architecture, model training, inference acceleration, and deployment-wit...
AlayaWorld Team, Kaipeng Zhang, Chuanhao Li et al.· arXiv.org· 1 citation
AlayaWorld is presented, an interactive long-horizon video world model that generates 24-fps video at 540p and 720p and introduces a discrete autoregressive distillation formulation that combines distribution-matching distillation, self-forcing++, and consistency distillation, reducing inference from approximately 30 s...
AlayaWorld Team Kaipeng Zhang, Chuanhao Li, Y. Zhan et al.· arXiv.org· 4 citations
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