Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are not designed to decode compressed representations directly. We present PrismCodec, which recasts a pretrained Diffusion Transformer (DiT) as a bitstream-grounded one-ste...
Yu-Nuo Chen, Jing-Nan Gao, Bing He et al.· 0 citations
Reconstructing 3D scenes from sparse views without per-scene optimization remains highly challenging, especially for recovering accurate geometry and fine textures. While recent feedforward approaches leverage generalizable 3D Gaussian Splatting (3DGS) for scene generation, they typically assign one or multiple Gaussia...
Bing He, Jing-Nan Gao, Yu-Nuo Chen et al.· 0 citations
3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remains difficult to produce lightweight, editable, and topologically clean artistic content that is directly production-ready. To achieve this, we present TaoFlowForge, an ar...
Xian-Ze Fang, Qi-Yuan Feng, Dongfang Sun et al.· 0 citations
This work introduces HarnessEval-W, an agentified evaluation pipeline that brings the harness paradigm from the LLM ecosystem to world model benchmarking, and applies HarnessEval-W to 18 representative world models over 330 evaluation cases.
Wei-Liang Chen, Haowen Sun, Jun Gao et al.· 3 citations
Enfold is presented, which transfers this computation into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations: its future need not be materialized at every step if its internal structure can be enfolded into...
Wei-Li Zeng, Yi-Tong Xing, Fu-Long Liu et al.· arXiv.org· 0 citations
This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.
Wei-Li Zeng, Yi-Tong Xing, Fu-Long Liu et al.· 1 citation
A unified spatiotemporally decoupled framework named DeMoDiff is proposed, which jointly redesigns representation and architecture and incorporates spatial-temporal masking and attention mechanisms into an autoregressive diffusion generator, achieving both generative capability and controllable editability.
Cheng-Qun Yang, Liang Xu, Yan-Ping Li et al.· 0 citations
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