Compact token sequences are essential for efficient 3D generation. However, existing 3D tokenizers typically organize latent representations either over spatial regions or as fixed-size sets of global tokens, both suffering sharp reconstruction degradation when compressed to extremely low token budgets. In this paper,...
Mingda Lin, Weijie Wang, Zeyu Zhang et al.· 0 citations
Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual creation. A program can execute correctly while violating the requested composition, appearance, or motion. We define this discrepancy as the Program-to-Visual (P2V) gap an...
Hao-Yu Zhao, Zi-Hao Zhang, Xu-Dong Wang et al.· 0 citations
Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture rai...
Hao-Yu Zhao, Zi-Hao Zhao, Tian-Yuan Deng et al.· 0 citations
Block3D is proposed, a block-wise diffusion framework that partitions the discrete shape-token sequence into contiguous blocks, generates the blocks autoregressively, and jointly denoises all tokens within the current block and introduces confidence-guided intra-block correction, which revises low-confidence tokens bef...
Bowen Cui, Weijie Wang, Zeyu Zhang et al.· 1 citation
Despite the success of diffusion models in Video Frame Interpolation (VFI), existing methods still suffer from two critical limitations. First, latent diffusion inevitably loses fine-grained details when reconstructing images from latent representations back to the pixel space. Second, multi-step sampling incurs prohib...
Zihao Zhang, Haoyu Zhao, Siqian Yang et al.· arXiv.org· 0 citations
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