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most citedTactile DreamFusion: Exploiting Tactile Sensing for 3D Generation

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cs.CV2025

Xiaomi MiMo-VL-Miloco Technical Report

Jiaze Li, Jingyang Chen, Yuxun Qu +9

We open-source MiMo-VL-Miloco-7B and its quantized variant MiMo-VL-Miloco-7B-GGUF, a pair of home-centric vision-language models that achieve strong performance on both home-scenar…

cs.CV2025

Part-X-MLLM: Part-aware 3D Multimodal Large Language Model

Chunshi Wang, Junliang Ye, Yunhan Yang +6

We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RG…

cs.CV2025

PAT3D: Physics-Augmented Text-to-3D Scene Generation

Guying Lin, Kemeng Huang, Michael Liu +8

We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision-language models with physics-based simulation to produce physically pla…

cs.CV2025

Generating Physically Stable and Buildable Brick Structures from Text

Ava Pun, Kangle Deng, Ruixuan Liu +3

We introduce BrickGPT, the first approach for generating physically stable interconnecting brick assembly models from text prompts. To achieve this, we construct a large-scale, phy…

cs.CV2025

Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization

Kangle Deng, Hsueh-Ti Derek Liu, Yiheng Zhu +7

Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disre…

cs.CV20241 cited

Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation

Ruihan Gao, Kangle Deng, Gengshan Yang +2

3D generation methods have shown visually compelling results powered by diffusion image priors. However, they often fail to produce realistic geometric details, resulting in overly…