DataEvolver introduces a reusable framework with generation-time self-correction and validation-time self-expansion loops that improves visual datasets, shown to outperform baselines on an object-rotation task.
Seed3d 1.0: From images to high-fidelity simulation-ready 3d assets.arXiv preprint arXiv:2510.19944, 2025
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CG-MLLM is a multimodal LLM using a Mixture-of-Transformer architecture with separate TokenAR and BlockAR components integrated with a pre-trained vision-language backbone and 3D VAE to enable 3D captioning and high-fidelity generation.
Home3D 1.0 describes a four-module image-to-3D system using latent SDF, flow-matching, texture fields, material retrieval, and part-specific VAEs to produce meshes with PBR materials and decomposable components.
citing papers explorer
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DataEvolver: Let Your Data Build and Improve Itself via Goal-Driven Loop Agents
DataEvolver introduces a reusable framework with generation-time self-correction and validation-time self-expansion loops that improves visual datasets, shown to outperform baselines on an object-rotation task.
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CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
CG-MLLM is a multimodal LLM using a Mixture-of-Transformer architecture with separate TokenAR and BlockAR components integrated with a pre-trained vision-language backbone and 3D VAE to enable 3D captioning and high-fidelity generation.
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Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design
Home3D 1.0 describes a four-module image-to-3D system using latent SDF, flow-matching, texture fields, material retrieval, and part-specific VAEs to produce meshes with PBR materials and decomposable components.