Dataset
JeffreyXiang

JeffreyXiang/TRELLIS-500K

TRELLIS-500K TRELLIS-500K is a dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores. This dataset serves for 3D generation tasks. It was introduced in the paper Structured 3D Latents for Scalable and Versatile 3D Generation. Dataset Statistics The following table summarizes the dataset's filtering and composition: NOTE: Some of the 3D assets lack text captions. Please filter out such assets if… See the full description on the dataset page: https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K.

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Thành tựu nổi bật

Những danh hiệu mới nhất của dự án

1
TINIX TRENDING·Chủ đề: arxiv:2412.01506

#1 Repository Của Tháng

1
TINIX TRENDING·Chủ đề: arxiv:2412.01506

#1 Repository Của Tuần

1
TINIX TRENDING·Chủ đề: task_categories:text-to-3d

#1 Repository Của Tháng

📦 TRELLIS-500K: Bộ dữ liệu 500K tài sản 3D chất lượng cao cho sinh 3D

Bộ dữ liệu 500K tài sản 3D được tổng hợp từ năm nguồn khác nhau, lọọc bởi điểm số thẩm mỹ, phục vụ trực tiếp cho các tác vụ sinh 3D.

TRELLIS-500K là một bộ dữ liệu gồm 500.000 tài sản 3D được tổng hợp từ Objaverse(XL), ABO, 3D-FUTURE, HSSD và Toys4k. Tất cả các mẫu dữ liệu đều được lọọc dựa trên điểm số thẩm mỹ để đảm bảo chất lượng. Bộ dữ liệu này được giới thiệu trong bài báo "Structured 3D Latents for Scalable and Versatile 3D Generation" và phục vụ cho các tác vụ sinh 3D từ hình ảnh hoặc văn bản.

✨ Điểm nổi bật

  • Nguồn dữ liệu đa dạng: Tổng hợp từ Objaverse(XL), ABO, 3D-FUTURE, HSSD và Toys4k.
  • Lọọc theo điểm số thẩm mỹ: Đảm bảo chất lượng tài sản 3D cao.
  • Hỗ trợ nhiều cấu hình: Bao gồm cả bộ dữ liệu huấn luyện (500K mẫu) và tập đánh giá (Toys4k).
  • Cung cấp công cụ xử lý: Công cụ hỗ trợ trong TRELLIS toolkit.
  • Một số mẫu thiếu caption: Cần lọọc ra nếu yêu cầu caption.
  • Phân phối theo nguồn: Mỗi nguồn có ngưỡng thẩm mỹ và kích thước lọọc khác nhau.

💡 Vì sao đáng chú ý

Khác với các bộ dữ liệu 3D đơn lẻ, TRELLIS-500K tích hợp đa nguồn và lọọc chặt chẽ bằng điểm số thẩm mỹ, giúp cải thiện đáng kể chất lượng mẫu cho các mô hình sinh 3D. Việc cung cấp cả tập huấn luyện lớn và tập đánh giá chuẩn hóa làm cho bộ dữ liệu này trở nên hoàn chỉnh và dễ sử dụng.


#3DGeneration #TextTo3D #ImageTo3D #OpenDataset #TiniX

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